By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach to understanding it.
The new tool gives users a network diagram to reveal links as well as a high level report about what the network looks like. Using network analysis helped Google revolutionize search technology and was used by journalists who investigated the connections between people and banks during the Panama Papers Leak.
Connect the Dots is the fourth and most recent addition to DataBasic, a growing suite of easy-to-use web tools designed to make data analysis and storytelling more accessible to a general and non-technical audience launched last year.
As with the previous three tools released in the DataBasic suite, Connect the Dots was designed so that its lessons can be easily planned to help students learn how to use data to tell a story. Connect the Dots comes with a learning guide and introductory video made for classes and workshops for participants from middle school through higher education. The learning guide has a 45-minute activity that walks people through an exercise in naming their favorite local restaurants and seeking patterns in the networks that result. To get started using the tool, sample data sets such as Donald Trump’s inside connections and characters from the play Les Miserables have also been included to help introduce users to vocabulary terms and the algorithms at work behind the scenes. Like the other DataBasic tools, Connect the Dots is available in English, Portuguese, and Spanish.
Learn more about Connect the Dots and all the DataBasic tools here.
Have you used DataBasic tools in your classroom, organization, or personal projects? If so, we’d love to hear your story! Write to firstname.lastname@example.org and tell us about your experience.
This is a live-blog from the Data on Purpose / Do Good Data "From Possibilities to Responsibilities” event. This is a summary of what the speakers at the talked about, captured by Rahul Bhargava and Catherine D'Ignazio. Any omissions or errors are likely my fault.Human-Centered Data Science for Good: Creating Ethical Algorithms
Jake notes this is the buzzkill session about algorithms. He wants us all to walk away being able to critically assess algorithms.How do Algorithms Touch our Lives?
They invite the audience to sketch out their interactions with digital technologies over the last 24 hours on a piece of paper. Stick figures and word totally ok. One participant drew a clock, noting happy and sad moments with little faces. Uber and AirBnb got happy faces next to them. Trying to connect to the internet in the venue got a sad face. Here's my drawing.
Next they ask where people were influenced by algorithms. One participant shares the flood warning we all received on our phones. Another mentioned a bot in their Slack channel that queued up a task. Someone else mentions how news that happened yesterday filtered down to him; for instance Hans Rosling’s death made it to him via social channels much more quickly than via technology channels. Someone else mentioned how their heating had turned on automatically based on the temperature.What is an Algorithm?
Jake shares that the wikipedia-esque definition is pretty boring. “A set of rules that precisely deinfes a sequence of operations”. These examples we just heard demonstrate the reality of this. These are automated and do things on their own, like Netflix’s recommendation algorithm. The goal is to break down how these operate, and figure out how to intervene in what drives these thinking machines. Zarah reminds us that even if you see the source code, that doesn’t help really understand it. We usually just see the output.
Algorithms have some kind of goal they are trying to get to. It takes actions to get there. For Netflix, the algorithm is trying to get you to watch more movies; while the actions are about showing you movies you are likely to want to watch. It tries to show you movies you might like; there is no incentive to show you a movie that might challenge you.
Algorithms use data to inform their decisions. In Netflix, the data input is what you have watched before, and what other people have been watching. There is also a feedback loop, based on how it is doing. It needs some way to figure out it is doing a good thing - did you click the movie, how much of it did you watch, how many star did you give it. We can speculate about what those measurements are, but we have no way of knowing their metrics.
A participant asks about how Netflix is probably also nudging her towards content they have produced, since that is cheaper for them. The underlying business model can drive these algorithms. Zarah responds that this idea that the algorithm operates “for your benefit” is very subjective. Jake notes that we can be very critical about their goal state.
Another participant notes that there are civic benefits; in how Facebook can influence how many people are voting.
The definition is tricky, notes someone else, because anything that runs automatically could be called an algorithm. Jake and Zarah are focused in on data-driven algorithms. They use information about you and learning to correct themselves. The purest definition and how the word is used in media are very different. Data science, machine learning, artificial intelligence - these are all squishy terms that are evolving.Critiquing Algorithms
They suggest looking at Twitter's "Who to follow" feature. Participants break into small groups for 10 minutes to ask questions about this algorithm. Here are the questions and some responses that groups shared after chatting:
- What is the algorithm trying to get you to do?
- They want to grow their user base, and then shifted to growing ad dollars
- Showing global coverage, to show they are the network to be in
- People name some unintended consequences like political polarization
- What activities does it use to do that?
- What data drives these decisions?
- Can you pay for these positions? There could be an agreement based on what you are looking at and what Twitter recommends
- What data does it use to evaluate if it is successful?
- It can track your hovers, clicks, etc. both on the recommendation and adds later on
- If you don't click to follow somewhere that could be just as much signal
- They might track the life of your relationship with this person (who you follow later because you followed their recommendation, etc)
- Who has the power to influence these answers?
A participant notes that there were lots of secondary outcomes, which affected other people's products based on their data. Folks note that the API opens up possibilities for democratic use and use for social good. Others note that Twitter data is highly expensive and not accessible to non-profits. Jake notes problems with doing research with Twitter data obtained through strange and mutant methods. Another participant notes they talked about discovering books to read and other things via Twitter. These reinforced their world views. Zarah notes that these algorithms reinforce the voices that we hear (by gender, etc). Jake notes that Filter Bubble argument, that these algorithms reinforce our views. Most of the features they bake in are positive ones, not negative.
But who has the power the change these things? Not just on twitter, but health-care recommendations, Google, etc. One participant notes that in human interactions they are honest and open, but online he lies constantly. He doesn't trust the medium, so he feeds it garbage on purpose. This matches his experiences in impoverished communities, where destruction is a key/only power. Someone else notes that the user can take action.
A participant asks what the legal or ethical standards should be. Someone responds that in non-profits the regulation comes from self-regulation and collective pressure. Zarah notes that Twitter is worth nothing without it's users.Conclusion
Jake notes that we didn't talk about it directly, but the ethical issues come up in relation to all these questions. These systems aren't neutral.
I liveblogged a number of sessions while I was there. Read some of these to get a sense of how non-profits, official statisticians, and journalists are thinking about data. There was a particular focus on support the Sustainable Development Goals, but many of the comments and case studies shared have impacts for anyone working with data-driven decision making.
- Data Journalism
- Integrating Geospatial Analysis
- Data and Algorithm
- Data Advocacy and Impact
- Making Civil Society Data Literate
- Data Literacy: What, Why and How?
(This is not my post; it's a group effort with contributions from Catherine, Cindy, Emilie, Natalie, Nicole and Willow)
Some of the best lessons in technology, media and civics come from shared offline experiences in that intersection, and the weekend of January 19-21, 2016, brought plenty of that for some of us at the Center for Civic Media. In particular, our experiences in the Women's March (in different cities) gave us food for thought that we want to remember as time passes, and that's why we are keeping track of them here.
Here are some postcards and reflections shared by women in the Civic community:
Catherine's postcards from the Boston march:
From Cindy, reporting on the DC march:
"Planned Parrot-hood". I gazed at this family fondly thinking that they are here to protest like me. Then I read their sign. #democracy #differencesofopinionMy experience in DC, marching alongside my mother and sister, was remarkable and will certainly go down as one of the most meaningful experiences I have had. So many people flew in from all over the country. There was an openness to striking up conversation wherever/whenever -- whether on the train, at the rally, the march, then after at a restaurant, the next day and so on. This was likely partially because you could tell who was/had been protesting by footwear -- sneakers and clear backpacks all over the place, and certainly the pink hats! Remarks like “I’ve never been politically active in my life!” seemed to abound. While there were many white women, I thought there was good representation of ethnicities, differing perspectives, and men as well. Sadly, one of the pro-life contingents chose to yell over the rally speakers, which made it more difficult to foster mutual respect. Also, a group of indigenous women felt objectified/mis-appropriated as a group, as noted on Twitter. We drove down on Inauguration day. I found the subway traffic light and as such it was to hop the train on the Metro, and many people were wearing the red “Make American Great Again” hats. One elderly woman was wearing a red-blue-and-white outfit. She seemed so happy, and the cognitive dissonance I experienced given her demeanor and its significance was challenging and interesting. If I had more time than those 3 minutes on the train, would I ask her why she was so happy? Would I be able to engage in an objective and kind way? We are here because “we never thought we would have to fight for our rights. We are mothers and daughters and we are not happy that our reproductive choices are at stake. We are also protecting the future of our children. Trump’s appointees are atrocious.” Texas and Seattle representing. From Emilie, reporting on the Santa Fe march:
Just received this from Santa Fe and it's got my vote for best sign. "My taco is nacho business"
From Mariel and Natalie, reporting on the Boston march:
(Mariel) As we walked on the Longfellow bridge, surrounded by families who chose to do the same, the T (the Boston subway, that runs over the ground and over the river in this segment) slowed down and started honking the horn at us. The trains were packed, and signs were raised and pushed against the windows.
In absolute terms, I have been in protests as large as the Boston Women's March; but, then again, I guess that is not particularly special for a Mexico City resident. In terms of proportion, I knew as I witnessed this incident that it was the first time I got to live something this big.
Lots can be and has been said about the creativity seen in the different marches, from seas of pink hats to clever chants, but the stories of effort behind sign-making stuck with us:
- Mary is a quilter; "cutting out bits and pieces comes naturally". She has been attending peace gatherings for the last 15 years.
- Alicyn on the right is a former hairdresser, and she said this is the first time she feels bad enough to march. She is excited about the 100-day plan.
- Ellen and Mandy are the students behind the MIT sign-making session. They realized that there were probably many students, especially undergrads, that had never attended a protest in their life; and so they plotted to make a welcoming space where they could get fed, inspired to make signs and find marching buddies to stay safe during the march.
Jessica works at one of the major tech employers in Boston. Being from Singapore, she said she had never experienced an event like this, and we don't think she was just talking about the scale of it. It was an honor to get to share this experience with her.
(Mariel) The funny thing about the Women's March in Boston is that we didn't march. The largest public space and the planned route literally could not fit all the people who attended. And so, after an hour and a half of waiting to march, we gave up -- like many others. Point for protesting in Latin America: first we march, then we meet.
Rather than end, however, it would seem that the march filled up the restaurants nearby, Chinatown, and, eventually, the T stations -- full of signs and pink hats. On my journey back, a group of girls wearing pink hats, accompanied by two dads, caught my eye. They turned out to be the youngest daughters of a group of five mothers of 9th graders, all of whom went together to the march in DC. In their hat-making session, they made enough hats for the youngest ones who would stay in the city and attend this march instead.
When you lose faith in democracy, a ten-year-old on the subway might talk you back into it.
From Nicole, reporting from the march in DC:I have so many photos from the DC March. The rally / march / protest was inspiring and peaceful. The only problem was with some Trump supporters trying taunt the crowd but we didn’t take the bait (I have video). I did notice very little direction from the organizers and almost no security. I don’t think they were prepared for this size crowd. We also didn’t get to hear all the speakers due to technical issues and they ran 2 hours longer than planned.
A few of my favorite photos (more on Flickr) --
From Willow, reporting from the march in Seattle:”If you knew anything about my uterus, you wouldn’t keep picking fights with it!” The Seattle march was massive (135k last estimate I read), 25-40% bigger than anticipated. I really liked that the leadership and first speakers at our starting location made two main points: we are on occupied territory, and First Nations people will lead our march and to not get in front of them; and to have patience and space for others so we can take care of ourselves and each other on the march. It was also a “silent march,” meaning they encouraged people to not do chants. There were a few designated folk with megaphones who were “designated,” and who sang songs and had speeches along the way. This made it seem less adversarial somehow? The only thing that really stood out to me as needing improvement is that there was no closure at the end. No talks, booths, or bands or anything, so it just kind of… fizzled. And a final submission from the Civic community: The Chinese characters in the poster represents a Chinese dilate; means "chiding children, get out of my way." “President Trump is committed to eliminating harmful and unnecessary policies such as the Climate Action Plan and the Waters of the US rule. Lifting these restrictions will greatly help American workers, increasing wages by more than $30 billion over the next 7 years." We should get him out of the White House! Techie bonus: A datasheet with attendance estimates from the different Women's Marches.
This is a liveblog written by Rahul Bhargava at the 2017 UN World Data Forum. This serves as a summary of what the speakers spoke about, not an exact recording. With that in mind, any errors or omissions are likely my fault, not the speakers.This panel has four speakers on the topic of data literacy, with an emphasis on front-line, practical things. Empowering Future Users through Data Literacy - Professor Delia North Dean and Head of Math, Statistics and Computer Science in Universty of Kwazulu-Natal Durban. She wants to spread the message of empowering people (a theme for this session). Prof North, teaching over 30 years, works on curriculum design for school level teacher training. She has a passion for statics and youth, at the national level in addition to within her university. The need to maintain a competitive economy drives the need for statistical literacy from basic operations, to the PhD level. All citizens need basic statistical literacy, for basic citizenship; best to accomplish this while they are in school. Professionals need competence to use statistics effectively in the workplace. Specialists need to continually improve their practice. University tends to think everyone is on the path to becoming a mathematical statistician, but this is an old-fashioned approach. This isn’t developing them as “consumers” of statistics. Statistics is often introduced as “hidden” inside of mathematics, so this is what people in South Africa think about. That doesn’t identify it as a job opportunity to learners. In addition, statisticians are poor at marketing their discipline. It is viewed as difficult, boring and confusing. There is a shortage of skills, and an overestimation of ability. The best statisticians go to industry, so universities are left understaffed. There are “too few enablers” of statistical literacy. Data used to be scarce, but now it is everywhere. This requires a rethink of the way we introduce statistics. This involves bringing in more data, and teaching with new methods. Students need to be actively involved with working with large datasets. This is an opportunity, not a threat. The questions we ask on our assessments are calculator-driven, not focused on analytical thinking. Data literacy is an essential part of statical literacy. Decisions based on data should be part of the statistical literacy training. Statistics should be an applied mathematics applied within another discipline. For example, they collected rubbish with children and had them track the amount and graph it. You can’t keep it trapped in mathematics classes. You have to make learning these concepts fun! Engaging workshops can radically change how empowered a group of teachers feels to introduce statistics. They want to learn new teaching methods. You have to teach them at the beginning to introduce things in the right way. Empowering Users in Situ - Dr. Sati Naidu Executive Manager for Staekholder Relations for Statistics South Africa. Stats SA has moved away from selling the data to helping people use the data for making evidence-making decisions. In 1996 South Africa did its first census. The first CD they produced cost 100,000 USD. Now data collection is scattered across all the departments. That should all be available on one platform to drive decision making. They set up CRUISE, to merge a course for statistics, GIS, planning, and economics all together. Dr. Naidu attended this course and learned much about a geographic approach to statistics. Mapping can reveal patterns that are otherwise hidden in traditional analytical means. This is demonstrated with a powerful set of maps that show the incidence of HIV/AIDS over time across Africa. Now Stats SA creates GIS to create a platform to combine geometry, shape-files, and more. This lets them create thematic maps very easily. They offer trainings on these tools throughout South Africa. Another example is looking at piped water over time, to see an increase. With the map you can see which areas improved, and look for patterns in those with low or high services. You can run hotspot analysis to look at unemployment data. You can do geospatial analysis to look for outliers and then look for causes. When data is non-stationary you can’t just use traditional statistical analysis. For instance new houses are much more expensive than old houses in most of Cape Town. But in one area, new houses are very cheap because of the location. So in one part of town there is a positive correlation, and in another there is a negative one. You can find this with geographically weighted regression (GWR), while it would be hidden in a traditional regression. Stats SA has all the official data. Now they want to engage with private providers to make their data available. We need to change from Big Data to Open Data, to go from its size to how it is used. Data Literacy for Capacity Building - Dr. Blandina Kilama Dr. Kilama works for REPOA on Poverty Research in Tanzania. REPOA is a think-thank in Tanzania that undertakes policy research. She also teaches statistics part-time, and will share some of her learnings from there. The stakeholders vary form Policy Makers, to Academia, to Media, to CSOs. Tanzania, has agriculture, This matters when politicians and others often conflate things like employment and productivity when talking about growth. Most African countries are seeing growth from productivity, not from labor. For instance, agriculture, industry and services contribute roughly equally in terms of the economy. However, more than 70% of the labour force works in agriculture. This capacity causes problems sometimes. For instance REPOA produced some poverty maps that were used by policy makers, leading to reactions of surprise and accusations. Spatial analysis helped them explain this better, but showing how districts next o cities experience growth, while districts next to refugee camps showed lack of growth. For media, REPOA builds in flexibility. They do half-day trainings, and make topics relevant for their current work. These fit the media workers schedules, between their morning checkins and afternoon deadlines. The challenges include weak numerical literacy, a shift in policies, and a lack of time. In Tanzania there is a common saying “we are all scared of numbers.” This attitude is a real social challenge to conquer; the stakeholders have a deep fear of numbers. Policies need to shift to include the idea that people providing the data are protected, and experience benefits from it. Data and Statistics: the sciences, the literacies and collaboration - Professor Helen MacGillivray Dr. MacGillivray is a high-level mathematical statistician, and heavily involved with teacher training. Works in Australia, but is the incoming President of International Statistics Institute. This is a big topic, and the challenges reflect that. In Australia, the people involved in teaching are the ones thinking about what is data literacy, and what is data science. There are valuable lessons in the decades of work on building statistical literacy. The include work within the other disciples. Some tidbits include the idea that descriptions are better than definitions, and that discussion is essential, but diagrammatic representations are not. Statistical literacy focusing on understanding, consuming information, and interpreting and critically thinking about. This differs at grade levels. The curricula has an aim of helping you look behind the data, ask why it is presented, and what questions can be asked. With data literacy there aren’t many definitions around. The ones that exist vary. Some split this between information literacy and data management. Why is this important? It is for everyone to the extent appropriate for their level of education, training, and work. This is very contextual, so it is a constant learning. How do you do this? Models at the governmental level are actually decades old. The emphasis is on the problems, the plan, getting the data, analyzing, and then discussions and interpretation. Dr. MacGillivray, in her workshops with teachers, encourages them to not think about the problem and the answer. This work is much wider than that. At the professional level, current approaches lead statisticians to think that they should NOT be involved with the collection of data; that somehow that gets their hands dirty. They think it is a waste of a statisticians valuable time. Nothing could be further form the truth! In terms of penetration, there is lots of practice, but current teaching methods are still buried in old practices. They need to use complex, many-variabled datasets. This leads to impediments for data literacy and data science. Instead of a misplaced focus on calculation as in staticialy literacy education, in data science education there is a misplaced focus on coding. Q & A How about grassroots data literacy - what school do I send my students to? can students analyze air quality? Part of data literacy is knowing data is important for decisions making. Prof North responds about the import of sourcing of data, what it is, where it came from, why it was collected is critical. Now we try to use household data that is from the world of the student. You can use larger datasets, but still from the world of the student. In terms of data availability, is there a way to asses the data literacy levels of different countries? How can we do better outreach? Prof Naidu responds that, In terms of dissemination, now Stats SA takes the data to the people. They have huge publicity campaigns to argue for collection; and then takes the results back to the people. The SDGs combine social, economic, and environmental measurements. The average person on the street that is the target for behavior change, needs to understand the links between the three. Where does scientific literacy come into this? Prof MacGillivray reminds us that this is an old question, because these literacies operate within context in other fields. We have to work with other disciplines and their educations. Prof North adds that at her university they implemented practices that try to involve the other disciplines. So if a student came in for help from another department, they involved the supervisor. Dr. Kilama adds that in her country collecting the environmental data collection is the challenge they face. Using data literacy as a means to protect poeple from fake statistics. VIsualization can make bad statistics very acceptable. We need to educate people about how to differentiate between good data and good-looking data. This is the focus of the critical approaches.
I reached out to friends at Center for Civic Media about how much I've been hearing lately about folk wanting to "pop communication bubbles." A bunch of these (and Berkman) folk have been working on things like that for a long time, and have some excellent things to share in regards to our attempts, successes, failures. This is a near-exact transposition of their response to my prompt.
Platforms which already try to bridge political (or other) differences:
- Mic.com, which started as PolicyMic but pivoted / gave up
- Wael Ghonim's https://techcrunch.com/2016/01/28/inside-parlio-egyptian-activist-wael-g...
- Systems that give people feedback on their news reading behavior (most haven't worked very well)
A review of these systems is in
- Matias, J. N., Szalavitz, S., Zuckerman, E. (2017) FollowBias: Supporting BehaviorChange Toward Gender Equality by Networked Gatekeepers on Social Media.In Proceedings of the 20th ACM Conference on Computer Supported CooperativeWork & Social Computing. ACM Press, 2017 http://natematias.com/media/research/FollowBias_CSCW_2017-Matias-Szalavi...
- Sean A. Munson, Stephanie Y. Lee, and Paul Resnick.2013. Encouraging Reading of Diverse Political Viewpoints with a Browser Widget.. In ICWSM.http://dub.uw.edu/djangosite/media/papers/balancericwsm-v4.pdf
- Q. Vera Liao and Wai-Tat Fu. 2014. Can you hear menow?: mitigating the echo chamber effect by sourceposition indicators. In Proceedings of the 17th ACMconference on Computer supported cooperative work &social computing. ACM, 184–196.http://dl.acm.org/citation.cfm?id=2531711
- D'Ignazio, Catherine. 2014. Engineering serendipity : TerraIncognita and other strange encounters with global news.Thesis. Massachusetts Institute of Technology.http://dspace.mit.edu/handle/1721.1/95597
- Siamak Faridani, Ephrat Bitton, Kimiko Ryokai, and KenGoldberg. 2010. Opinion space: a scalable tool for browsing online comments. In Proceedings of theSIGCHI Conference on Human Factors in ComputingSystems. ACM, 1175–1184. http://dl.acm.org/citation.cfm?id=1753502
- Matias, J. Nathan, Elena Agapie, Catherine D’Ignazio, and Erhardt Graeff. (2014) Challenges for Personal Behavior Change Research on Information Diversity. Workshop on Personalized Behavior Change, at The 32nd ACM Conference onHuman Factors in Computing Systems (CHI’14). http://http://personalizedchange.weebly.com/position-papers/challenges-f..."bad url"]
https://unfold.com/ breaks news into simple statements, lets users vote their opinion
Things which indicate how great Amber is and that it should be used, but I bet were great when they led somewhere:
- http://newschallenge.tumblr.com/post/19479653130/differentfeather ["not found"]
- http://newschallenge.tumblr.com/post/19491857457/popping-the-filter-bubb..."not found"]
- http://lietome.babelon.co/ [-->Go Daddy page
Cheers, and happy informed making!governmentsocial networkstechnology solutions
In the US, NFL football is more than a sport - it’s a stage on which broader national dramas play out. In the past years, the NFL has brought to national attention conversations about domestic violence, about cheating and fairness and about the ethics of loving a sport that is likely killing its players. With Colin Kapernick’s decision not to stand for the singing of the national anthem during a pre-season football game, starting a wave of similar protests by athletes, a national debate about endemic racism in the US has now become a debate about race, protest, politics and NFL football.
Some years ago, journalist and activist, the late Dori Maynard posed a question to the Media Cloud team: Does sports media use different language to talk about black and white athletes? The question, Dori told us, came from basketball player Isaiah Thomas, who had observed that journalists often described black athletes as physically talented but talked about the intelligence of white athletes. While both descriptions are laudatory, they focus on different aspects of a player's talents, and enforce long-standing racial stereotypes about intellect and physicality. Could Media Cloud, Dori wondered, put some numbers to these anecdotes?
This isn’t a new research question. Scholars have analyzed the language play-by-play announcers use and have seen the patterns in which white players are praised for intelligence and black players for physical attributes. (See also Rainville and McCormick, 1977 and Rada 1996) Media Cloud gives us the chance to analyze a different corpus, sports stories written after the game, and to examine this possible phenomenon on a different scale. We focused our study on the attention paid to and language used to discuss NFL quarterbacks, the most highly paid and most discussed players on the field.
So do we talk about white quarterbacks as intelligent and black quarterbacks as athletic? Well, like almost everything involving media and race, it's complicated.
First, we talk a great deal about football in the US media. We analyzed tens of thousands of stories from 478 publications (including US sports websites like NFL.com as well as national and regional sources) over 4 months of NFL regular season coverage in 2015.Despite the prominence of stories like , the vast majority of writing about football discusses this week's results, next week's matchup and teams' strategies for success. As a result, the table of word frequencies when we talk about quarterbacks is heavy on two kinds of words: words that describe gameplay, and words that describe injuries.
We’ve classified each of the 53 quarterbacks who played in NFL games last season as white, black or hispanic (using data from the besttickets unofficial NFL player census, acknowledging that these categories are socially constructed, complex and overlapping.) We then examined what words are associated with coverage of white QBs and QBs of color. In general, white QBs were slightly more associated with action words - ran, threw, leapt - and non-white QBs with words about their health and bodies, their off-field lives and descriptive words, like “dominant” or “judgement”. (Our handcoding of the top 250 words associated with QBs, and synonyms for those words, is here.)
We further examined what words were disproportionately associated with white and non-white QBs. For instance, the words “Heisman” and “trophy” were more than three times as likely to appear in stories about black QBs than about white QBs, likely because Heisman winning black QBs Marcus Mariota and Jameis Winston played more last year than white Heisman winner Johnny Manziel. Some of those terms do suggest a focus on the physicality of black QBs:Word used more with black QBs Usage note “Mobile” 2.48x “Threat” 2.46x (aka: “dual threat” to run or pass) “Legs” 2.03x “Runner” 2.00x “Scrambling” 1.97x “Rushing” 1.92x “Sliding” 1.87x “Speed” 1.84x “Balance” 1.84x (may refer to a “balanced offense” as well as to the physical characteristic)
Words disproportionately associated with white quarterbacks tend to characterize specific scandals and controversies. In most cases, these words describe only one or two quarterbacks, whereas the words disproportionately associated with black QBs often describe multiple players:Word used more with white QBs Note “Deflated” only associated with Tom Brady “Charter” only associated with Ryan Mallett missing a charter flight “Court” “Hormone” “HGH” “Jazeera” An Al Jazeera story about possible use of human growth hormone in the NFL
Words associated with both white and black quarterbacks, but disproportionately with white QBs also include “domestic” (ie., domestic violence) and partying.
Before concluding that US media is somehow biased against white QBs and their scandals, it’s worth keeping in mind that these terms disproportionately associated with white QBs are highly idiosyncratic - they’re more the portrait of a single player’s struggles than the way a whole group of players are characterized. Moving down in the frequency table to words that appear 1.5x to 5x more with white QBs than black QBs, we find some evidence to support the “white brains, black bodies” hypothesis, but less than we expected.Word used more with white QBs Usage Note “Slipped” 4.3x “Slow” 4.2x “Prepared” 2.3x “Practice” 2.1x “Caller” 1.9x (“signal caller”) “Steady” 1.7x
If there’s no racial smoking gun in looking at word frequencies, it may be because, as John Caravalho put it, “No broadcaster or sportswriter this side of Rush Limbaugh is so self-destructive as to blatantly muse on the suitability of a black quarterback.” Reporters may be increasingly sensitive to issues of word choice. But the amount of attention paid to white versus black QBs tells a somewhat different story.
We analyzed how much media attention each of the 53 quarterbacks in our study received. To adjust for the fact that some quarterbacks in our set played very few minutes, we calculated words per minute played, a statistic that ranged from 25.5 words/minute for Titans backup Zack Mettenberger, to 471.4 words/minute for the Cowboys Tony Romo, who suffered a shoulder injury and missed most of the season, to the great dismay of the Dallas press. While Romo is the largest outlier in the set, five other quarterbacks - all white - received unusually high words per minute scores: Brandon Weeden, Johnny Manziel, Landry Jones, Peyton Manning and Tom Brady. The first three - Weeden, Manziel and Jones - played very few games - Jones was a substitute in a single game, while Weeden and Manziel started fewer than 3 games in a 16 game season - skewing these counts. Manning and Brady are "name-brand" quarterbacks, who received additional attention in 2015, Brady for the ongoing "Deflategate" saga and Manning for winning the Super Bowl and retiring.
Comparing a quarterback's passer rating to his words of coverage suggests that "name brand" quarterbacks are at a distinct advantage in terms of media attention. Six quarterbacks - five white, one black - appear as outliers in this chart. (Romo, who we code as "Hispanic", didn't play enough minutes in 2015-16 to have a QB rating.) Peyton Manning, Aaron Rodgers and Tom Brady are all elite quarterbacks who are also recognizable public figures, endorsing products and commanding media attention. (All receive more than $6m in endorsements per year, and rank #1, #4 and #5 in the list of QBs ranked by endorsement money in 2015.) Manziel's disproportionate attention springs from notoriety - he was benched after videos surfaced of him partying during a bye week - while Andrew Luck had an injury-plagued season that was both poor and widely discussed. The only black quarterback who is an outlier in this set is Marcus Mariota, who outperformed expectations for the Titans, and generated widespread hand-wringing in Tennessee when he was injured late in the season. Notably, the year's best-rated quarterback - the Seattle Seahawks' Russell Wilson - is black, and received significantly less attention than worse-rated "brand name" quarterbacks, though average attention for his rating as predicted by our model. Like Manning, Rodgers and Brady, Wilson makes more than $6m a year in endorsements, but his financial success doesn’t lead to disproportionate coverage. Nor does it lead to overcoverage of Drew Brees and Eli Manning, white QBs who were #2 and #3 on the endorsement list in 2015.
Given the messy relationship between performance and attention, we asked whether a naive hypothesis - that sportswriting coverage tracked actual performance - might help answer Dori and Isiah's question. If black quarterbacks tend to be described as "athletic", might it be in part because their athleticism is more impressive than that of white quarterbacks?
We looked at two statistics to try to calculate "athleticism": the 40 yard dash and rushing yards gained by the quarterback. White quarterbacks averaged a little over 4.8 seconds on the 40 yard dash, while black quarterbacks averaged a little below 4.6 seconds. In the NFL, that .25 second gap is an eternity - black quarterbacks, on average, run nearly as fast as receivers, the fastest players on the field, while white quarterbacks are closer to linebackers. That speed apparently matters, as black quarterbacks averaged a little over 200 rushing yards in a season, while white quarterbacks generally had fewer than 50.
This finding about differences in athletic ability by race is obviously heavily loaded, given the long history of racist speech that portrays blacks as fundamentally physically different than whites. We note that the system that results in the presence of more athletic black quarterbacks than white quarterbacks in the NFL is a highly complex one that is deeply embedded in the racial mores of our society. This piece on how modern NFL quarterbacks are made finds that the top 15 quarterback prospects in the 2016 draft overwhelmingly: started playing quarterback by age 9, came from stable families in homes worth at least the median home value, had outside coaching starting in high school, and participated in year round formal 7v7 programs. This kind of intense, adult driven athletic experience is much more common in suburban communities than urban communities. For one example, his piece on the "Hidden Demographics of Youth Sports" lists the five states with the lowest rate of high school sports participation, and four of those five are among the states with the most black households. All of this is to say that this data on the athletic advantage of black over white quarterbacks may or may not say anything about inherent athleticism of black people but almost certainly says something about the deeply racially infused cultural systems that produce modern professional athletes.
Given all of the above, there's an argument that black quarterbacks are genuinely more athletic - at least in terms of foot speed - than white quarterbacks, and the differences we see in language about quarterbacks may correlate to their performance. That may run counter to suspicions that led Dori to ask her question. But we did find a way in which there's an apparent racial disparity in coverage: sheer attention.
Only eight quarterbacks broke the 40,000 word barrier in our set, two black, one hispanic, five white. Set the bar at 50,000 and we're down to four white QBs and Tony Romo. At the highest levels of attention, four "name-brand" quarterbacks (Rodgers, Brady, Manning, Romo) and one screw-up (Manziel) dominate discussion of football in 2015-6. Elite black QBs - Russell Wilson, Marcus Mariotta, Cam Newton - received more attention than mediocre quarterbacks, but less than name brand, endorsement laden white QBs, despite in Wilson's case, significantly superior performance.
Is there a racial bias in sportswriting about the NFL? Probably.That bias may be related to which NFL players gain endorsement contracts and widespread celebrity, and which ones fall short of expectations to reach that elite level. It’s difficult to entangle causality, though - all but one of these “name brand” QBs are white, and we may pay attention to them because of their celebrity, which correlates only partially to their superior athletic performance, and may correlate more closely to their race.
We will be updating our study at the close of the 2016-7 NFL season, and are looking forward to seeing whether Kapernick’s protest challenged the attention patterns we saw in the previous season.
This post was written by Ethan Zuckerman in collaboration with Allan Ko, Rahul Bhargava, and Hal Roberts. Allan Ko produced the graphics and conducted the quantitative research.media
Every year, Canada's Médecins Sans Frontières (AKA Doctors Without Borders / MSF) meets for their Annual General Assembly. I know about this because two years ago their topic was "Is MSF missing the technology boat?" to which I was invited to speak about Geeks Without Bounds and community technology projects with the talk "Technology as a Means to Equality" (video broken because of issues with GWOB YouTube account, and with my apologies). I went back this year because my organizational crush on them maintains, and because Aspiration (my employer for teh past 2 years, a technology capacity building organization for nonprofits) has been working on an ecosystem map of the digital response space. The real-world and values-driven experience of MSF provided valuable insights and data points for that map, and so I went seeking their input. I spent the first day at their Logistics Day hearing about 3D printing for manufacturing and prosthetics, telemedicine, and use of smart phones. My second day was for the Annual General Assembly again, this time as an attendee. The first half of the day focused on how to deal with the bombings of hospitals in war zones, the second half on mental health for patients and for field practitioners. I'd like to speak to you here about the first half of that day, how it overlaps with things I've learned here at the Center for Civic Media, things I was reminded of during bunnie and Snowden's announcement at Forbidden Research, and things which have sadly continued to be relevant.#NotATarget
There are certain things that humanity has learned we find untenable from past experience. Some of these lessons are most notably codified in the Geneva Conventions, ratified by the UN and its membership. Among other things, the Geneva Conventions cover how noncombatants should not be involved in conflict, the right to bring someone to trial for war crimes, and the right to access to medical treatment. This last is of most concern of MSF, namely in that more and more hospitals in war zones are being bombed. These bombings are happening without the external accountability which the Geneva Conventions and the UN Security Council claim to uphold (then again, 4 of the 5 permanent seats on the UN Security Council are held by countries linked to these bombings, so that's maybe a conflict of interest and integrity). So, maybe this is not a system of accountability we can necessarily depend on any longer. How can the bombings be stopped? Who can (and should) hold those doing the bombings accountable, if not the long-standing (albeit imperfect) Geneva Convertion mechanism? MSF has been maintaining a campaign for public visibility, hoping this will lead to some level of accountability via #NotATarget.
The question that I remembered during bunnie and Snowden's announcement at Forbidden Research was: are these individual blips of horror across many different countries, or is this a new norm? bunnie and Snowden were referring to the subtle but systemic targeting and killing of journalists. MSF panelists spoke of the picking off of hospitals in conflict zones, sometimes when nothing else around them has been attacked. There are two things at play here: a technological way to do the targeting, but also the acceptance of this happening. One speaker at MSF AGA, Marine Buissonniere, spoke to both of these points by indicating that we should be able to hold both highly contextual circumstances and overall trends in mind at the same time. Perhaps the bombing of one hospital happened in a silo of decision making for one military, but the fact that it is deemed acceptable by so many at the same time indicates a deeper shift in global cultural norms. She and the other panelists also spoke to how this is an ongoing attack on civic life, that hospitals are the last refuge in times of war, and to make them unsafe is to remove the provision of basic needs to entire regions. Whether a subtle cultural shift or a concerted effort to errode transparency, accountability, and safety; both the cases of hospital bombings and targeted journalist killings come from a similar place of disregard for human life and for accountability. It is our responsibility to hold those taking these actions accountable.
It can be difficult to understand what this sort of thing means. It is difficult to build empathy, a huge component in bringing sufficient public attention on those trampling human rights to hold them accountable. The panelists acknowledged "outrage fatigue" coupled with the failure to act from enforcement agencies and courts. When our attention wanes, so too do the mechanisms of accountability. Similar to the work already done around Media Cloud and Catherine's work on location of the news, a question emerged: would people have cared differently if these bombings were happening a part of the world other than the Middle East and North Africa? Regardless, those perpetrating these violences are benefiting from our outrage fatigue. How can we take care of ourselves and each other while balancing our areas of influence with our areas of concern? How do we choose which actions to take?Journalism and Medicine
This is in part why I think MSF is so amazing. They act both to respond to a basic human need (access to medical care) in places often abandonded or never paid attention to begin with, and they speak truth about the circumstances in which they do so. To publicly statewhere a hospital is both puts them in immense danger and also protects them through public outcry against that danger... but only if those outcries continue to occur. To seek justice for infractions to human rights can be seen as non-neutral, which would then put MSF deeper in harm's way.
One way to navigate this might be divvying up parts of this ecosystem. Diederik Lohman from Human Rights Watched joined the panel to speak about documentation and accountability -- documentation which MSF practitioners are not trained to create, and the creation of which might jeapordize their status as neutral parties. If instead someone from Human Rights Watch were to document, could MSF better maintain their role as a humanitarian, and therefore neutral, party?Truth is the first casualty of war
Many of the atrocities associated with #NotATarget remain unaccounted for due to politics. But some have to do with a lack of visibility of the incidents and others of the context of the incidents. MSF often doesn't disclose the nationalities of their clinicians as a way to emphasize that all tragedy is human tragedy, rather than allowing countries to cherry-pick reporting based on what seems connected to them. But they're also not great at indicating how many different demographics across civilians and combattants they've served on a given day. The impartial nature of their service delivery is invisible to both local and international crowds. What would documentation look like which helped MSF do its job, made disruptions of that work visible in a trusted way, and wouldn't add to the reach of the surveillance state?
I came away from both of these sessions with more questions than I arrived with, but also greater trust and awareness of the others doing work in these spaces. All my best, in solidarity and hypoallergenic kittens, for all that you do.activismjournalismmedia
Election parties that turned into funerals. Sleep-deprived humans floating through the street, numb after a weeknight of crying, alcohol, or both. Silence: the morning of November 9, 2016, in Cambridge, Massachusetts, was frighteningly silent. For all the rationality, all the number-crunching, all the exploration of electoral scenarios, dealing with elections remains a deeply emotional task.
The task for many on November 9 was to find ways to help themselves and others process those emotions. As it happens, I had to start that very day with a class I was meant to facilitate. On civic media, of all things. In 2016, can the discussion around civic media provide us with opportunities to process appalling outcomes, help us transition from the surreal to making sense, or does it just really rub salt into the wound?
I bypassed the never-ending slideshow and made a guide for discussion. I used the main points of the Civic Media: Technology, Design, Practice chapters assigned for this class by professor William Uricchio (see at the end of the post), and framing questions I learned from workshops with feminist organizations in Mexico.
For something kind of thrown together in the last minute, I think it ended up providing opportunities for catharsis, discussion, and for collectively remembering that there is a path forward, and I want to keep a copy of it for when I am back home and dealing with elections in 2018. After seeing mail after email (in both activism and academic spaces) asking for ideas on ways to create spaces that can help people move forward this week, and as progressives in the US go back home for holidays, I thought I would share this.
Like with all curriculum creation, this module reflects things I modified after the first run – if you end up using it, please consider adding your feedback.
How did it go for us? When talking about fears, the different stages and paths of everyone in the room helped us cover our fears from the very individual access to services to the global impact of the electoral process, spending a long time talking about identity, our loved ones. When talking about democracy, the discussion shifted to lack of clarity and powerlessness, and the different roles ascribed to media throughout; and, when discussing participation, civic opportunities both within and outside the electoral framework were discussed.
More importantly, though, a lot of tears were shed in group; intimacy was built, even if just for an hour and a half; and, I hope, positive visions for going forward were exchanged. As Henry Jenkins, Sangita Shrestova et al wrote when discussing certain dance performances within frameworks of civic engagement, “Whether participatory or spectatorial, these spectacles do political work, empowering these movements to go out and change the world”. I like to think that, both in good and bad days, both public displays of emotion and discussions on media can do the same.
Related efforts, references, acknowledgements
One of the good things about big events like this is that they elicit positive efforts from tons of people, and here are some of the ones that I have found most inspiring:
– For more on conversation guides to use this season, keep an eye on Willow’s work.
– People creating spaces. While we had this discussion in class, other MIT students gathered to create a day-long space where people could process what happened. Participants there, too, were asked to discuss fears and hopes. Some professors, like Chris Peterson, held their classes there. Olivia Quintana wrote about it for Boston Globe.
– Educators thinking of ways to build the world they want to see and planning their next semester around it. Practitioners in the field of advocacy coming together to start reading groups on populism. Information security advocates discussing what the next steps are on the face of the election results. Progressive academics and practitioners talking about ways to go back to the communities and families that saw them grow up and that hold views very different from their own. This all is happening today thanks to the fact that someone sent a simple email to a list and got the conversation going.
– Let’s never underestimate the importance of using our voice to stand for human rights, for decency (a term pin-pointed this season by David Weinberger in a way that really resonated with me). Thank you, Shaun King, for keeping track of the instances of racism surfacing after the news; HRC, for standing with the LGBTQ community by sharing information about resources available; Everyday Feminism, for reminding everyone about the importance of self-care, and making it easier to do.
– Throughout the electoral season, and now, many inspiring social media posts have popped up on my feeds, and I am sure they have on yours, too. Let’s all take the time to thank those around us who have inspired us throughout. Extra points if you do it offline. But this is a topic I’ll leave for my personal blog.
– The texts that informed my discussion guide are On democracy and the digital age by Peter Levine; Superpowers to the people! How young activists are tapping the civic imagination by Henry Jenkins, Sangita Shresthova, Liana Gamber-Thompson, and Neta Kligler-Vilenchik; Partnering with communities and institutions by Ceasar McDowell and Melissa Yvonne Chinchilla, and Effective Civics by Ethan Zuckerman. All of these are chapters in Civic Media: Technology, Design, Practice, edited by Eric Gordon and Paul Mihailidis (2015, MIT Press).
Thanks to Kishonna Gray, William Uricchio, Usha Raman and Erhardt Graeff for helping me keep both activist and graduate student hats on for this post.electionsCivic media
One of the hardest lessons and ongoing challenges in digital disaster and humanitarian response is how to connect with a local population. While many digital response groups deal with this by waiting for official actors (like the affected nation's government, or the United Nations) to activate them, this doesn't always sit well with my political viewpoints. Some of these affected nations have governments which are not in power at the consent of the governed, and so to require their permission rankles my soul. But to jump in without request or context is also unacceptable. So what's to be done? It's from this perspective that I've been diving into how civics, disaster, and humanitarian tech overlap. And it's from this perspective that I've been showing up to Bayview meetings for San Francisco city government's Empowered Communities Program. ECP is working to create neighborhood hubs populated by members already active in their communities. Leaders in local churches, extended care facilities, schools, etc gather about once a month to share how they've been thinking about preparedness and to plan a tabletop exercise for their community. This tabletop exercise took place on October 20th in a local gymnasium.
The approach of ECP is generally crush-worthy and worth checking out, so I won't dive into it too much here. In brief, it is aware of individual and organizational autonomy, of ambient participation, and of interconnectedness. It has various ways of engaging, encourages others to enroll in the program, and lightens everyone's load in a crisis by lightening it in advance. I am truly a fan of the approach and the participants. It's also possible to replicate in a distributed and federated way, which means digital groups like the ones I work with could support efforts in understood and strategic ways.
Here is what doesn't necessarily show through in their website: how grounded in local needs and social justice these community members are. There is a recognition and responsibility to the vulnerable populations of the neighborhood. There is a deep awareness of what resources exist in the community, and of historical trends in removing those resources from a poor neighborhood in a time of crisis. We've had frank conversations about what they'll do about debris, and how the Department of Public Works parking and storage in their neighborhood is suddenly a positive thing. About what to do with human waste, and what a great boon it will be to have the waste water plant in their neighborhood. The things that wealthier parts of the city have vetoed having near them because of noise, pollution, and ugliness (NIMBY, or "not in my back yard") will make Bayview resilient. They're preparing to take care of themselves, and then to take care of other neighborhoods.
There's a plan in NYC now to knock on every. single. resident's door in the next crisis. It's an approach other cities might also consider. But it's one which is nearly impossible to implement. Who is doing the knocking? What are they doing with the information they gain? ECP's approach is to apply their own oxygen masks first, and then to check on their neighbors, to know what the local Hub can take care of and what is needed for external support. When/If a city employee comes knocking on their door, they can then speed up the process of getting aid to where it's needed ("I'm ok, but Shelly up the street has our 7 disabled neighbors there and they need a wheelchair, medication, and no-sodium food.")
Watching the Bayview community get together to prepare for the next crisis. Their resilience benefits us all. 💙✊🚀 https://t.co/Mi2KDFw2UN pic.twitter.com/g89L5UBAHS— Will-o-the-Wisp (@willowbl00) October 20, 2016
The end of the tabletop exercise had Daniel Homsey, the gent who heads up this program, talking about how we didn't devise plans while together, but we did learn how to suddenly have to work at another role with people we'd barely or never met before. And I, as a digital responder, listened to what the community's needs were, how they organized themselves, and considered the smallest interventions which could be maximally applied.crisis responselocal communitiesnetworks
(I promise this is one of the only two blog posts I will publish using primarily the first person.)
I am an activist, and technology and media are my favorite pretexts to start conversations about the core of our human experience. I love reflecting about concepts and their underlying ideologies, but asking teens whether they know someone who decided to untag themselves from a photo on Facebook is still my favorite way to ignite discussions on privacy.
I don’t believe in universal pedagogical statements about technology (I very much doubt everybody should learn to code), and part of my pride as an activist is in having developed a vision that allows me to be strategic about technology-based interventions. And yet nothing brings me more life than those epiphanic moments in tech workshops: the precise look (because I do think it is a look) that people get as they wrap their minds around the process.
I find it fascinating and challenging to be from a context where the dominant technology throughout my growth, the internet, permeates many of the central conversations in the public sphere. Attempts to address structures of power and inequality now look at the technological landscape as the ultimate opportunity to end history, maybe for real this time – and yet, upon digging, none of this feels new, nor particularly self-aware.
The mission that made me get up twelve years ago has matured, and it still makes me get up today. At age 14, it was about finding how I could use technology to make change. In my early twenties, about finding the affordances of media and technology to go beyond isolated events, building processes towards the world we wanted to see. Today, I want to be among those tracing the roadmaps for action to bring grassroots work to its ultimate consequences: improving the relationship between technology and society at the levels of infrastructure, policy, curriculum and public discourse.
How does work in digital literacies support youth development and what exactly should this work look like for each actor in the landscape? What are the participatory methodologies that best support knowledge creation and grassroots work? What are the values and judgements at stake in our discussions on privacy, security and surveillance? And, really, how can we talk about the ideologies underlying technology activism to stop seeing false opposites when we talk about freedom and protection, innovation and long-term development?
I was born and raised in Mexico, where I am privileged for my education, my professional circle, my sexual orientation, the color of my skin. I have a vision for the world I want to see, my consequent personal plan, and mentors and allies that constantly make my path easier than it was for them. Now I get to spend two years of my life reading, writing, thinking the thoughts I always wanted to think. So how can I make sure that, beyond intellectual stimulation, I can weave this richness into action?
I forgot to say: Hola! I’m Mariel Garcia-Montes, an incoming grad student in Comparative Media Studies, and a research assistant with Sasha Costanza-Chock at the Center for Civic Media at MIT. I will be occupying this blog for two years, and I am looking forward to seeing where all of us are standing – and where I will be standing two years from now. This (and other reasons I mention here) is why I wrote this post.
(Thanks to Rebecca Thorndike-Breeze from the MIT Writing and Communication Center for helping me improve this post. All the clear sentences and moments of perfect syntax are her fault; all the broken and unnecessarily complicated lines are mine.)youth
This is a liveblog of a talk given at the Center for Civic Media by Mushon Zer-Aviv (@mushon). Any errors or omissions are the fault of the authors - Rahul Bhargava and Catherine D'Ignazio.
Mushon is a design, educator and activist based in Tel-Aviv. He designed the maps for Waze, but has also worked on more contestational project like Ad-Nauseum. Mushon works at the intersection of design, tactical media and activism.
Mushon began this line of thinking in response to prompts from Tactical Tech Collective and the Responsible Data Forum. The theme is how to think about responsibility through the lens of visualization.
A network is made up of nodes (circles) and edges (lines). These get complicated quickly. At Waze, the street map was the network. Their need was to display how the algorithm thought about the network, vs. how we modeled the network in our head - ie. taking a non-obvious path due to the need to route around traffic. This is especially hard because we want drivers to look at the road, not the app.
In 1990, police shut down 42nd street in NYC for earth day. They tought this would increase traffic, but in fact it reduced it. This was based on a misunderstanding of the flow through the road network. This is called the "Braess Paradox" - where removing an edge can improve the flow of the network.
Paul Baran, a researcher at RAND, published a paper in the 1960's paper about options for centralized, decentralized, or distributed networks underneath the network. The question was how to design it to survive a nuclear attack. The decentralized network survived this attack best. This led to the TCP/IP network design. This is baked into the core of the internet today.
However today's flows across the internet are mostly through centralized nodes. We have constructed a myth of decentralized networks that is very popular. This is based on an attractive idea that everything is connected. If we could just count all of them then we could reveal "the big picture". If we can connect and analyze everything, we can get stuck in a graph of "network fetishism".
There are various ways to visualize these networks - arc diagrams, force-directed, etc. These layouts help us understand the structure of the network but do not help with understanding the flow or the protocols that govern the network. Nothing in the basic design of the distributed internet can help us understand how it functions today. A more representative diagram has far more connections per node.
A highly distributed network can still include centralized hubs. Optional centralization is a feature of a distributed network.
Nodes tell us what is connected. Edges tell us where the are connected. And flows tell us how they are connected. Protocols tell us why the network functions the way it does. These are the rules that "make sure connections actually work" (Galloway and Thacker).
Networks become the leading model and visual analogy of power and control.
Visualization is an important tool for critiquing our culture. TheyRule.net is an example of this. Hollywood has popularized the idea that understanding the links in a network of people can help you solve a crime. When the network becomes too big for a human to comprehend then a network analysis algorithm can take over. The NSA and other agencies use this same approach to network investigation.
We have an iconic image of a scruffy investigator staring at a corkboard of connections with pins and images. Now the cork board itself is doing the pinning and connecting.
Aspirational networking is this idea, that we can reveal everything with a network.
Networks aren't bad they are just drawn that way. That's what makes visualizing them so important. We see nodes and edges but normally we have to imagine flows and protocols. How to we make a network work better? How could we visualize them?
He show's Gilad Lotan's social media analysis of the Israeli invasion of Gaza. The story is mostly contained within the node structure - Israelis and Palestinians on social media were not talking to each other.
What happens when the flow is not comparable or quantifiable? He shows an image from Google Buzz, Google's experiment in social networking.
Directionality is important in text but in network diagrams it is hard to know where to start from. Where is the beginning? He shows an image from theafghanconflict.de. This mapped out the decision tree for what might happen.
We see the world as a narrative. The Panama Papers leak led journalists to try and map the network contained in the documents. Mushon animates drilling into the super complex network, zooming in a huge number for times. Some journalists mapped specific sub-networks around individuals of interest (Sergey Roldugin and Vladimir Putin). The Guardian told a narrative version of this story, an explainer, with video about how to hide a billion dollars. Mushon enjoys how it celebrates narrative with music and humor. These are missing in classic network diagrams.
He shows a revision of the Mossack-Fonseca network where he distinguishes between personal and business connections. If we follow the money, instead of the network, we can tell a narrative walkthrough of all the connections between the nodes, one at a time. Mushon talks through the story behind each node and edge as he adds it to the network. The narration followed the flow, rather than just being a static picture of how things are connected. This made "the network work for us".
Visualization is for humans; computers don't need this. To understand the hidden protocols behind this, we need to visualize the algorithms. This practice is currently mostly academic. The visual guide for machine learning is an example of how to educate us about the protocols that govern us, which he characterizes as "humanistic visualization".
We can and should visualize flows and protocols. We can visual insecure and secure version of TCP/IP's flows. Mushon shares another example that shows how Tor works.
Even if protocol visualization is limited it sets up an important expectation and possibly frustration. THere is something powerful politically for being frustrated for NOT understanding the protocol.
Getting back to Waze, only some of their ideas were taken to fruition. The effort to build self-driving cars is an attempt to take humans out of the loop completely. Mushon argues against the larger trend that removes human agency from computer-based systems. Our understanding and a computer's understanding are miles apart.
We need conceptual and visual tools to analyze networks. When we try to visualize this, and can't, power is lost.
A write up is available here: https://visualisingadvocacy.org/blog/if-everything-network-nothing-network
An audience member asks:
Larry Lessig's Against Transparency challenged how visualizations like the Panama's Papers one invite finding narratives that might not be there. There are a wealth of projects like this now. However, knowing that someone is connected to another doesn't imply there is a story there. If we are going to be doing more of this type of visualization, how do we tell if a narrative is really there?
Mushon replies that conspiracy theories are a huge danger for this type of investigative activism. There are so many possibilities of what one might find, that it is harder to do our job. We see potential there, but the network doesn't show what is actually happening. It inspires use to complete the picture ourselves. People are excited about network diagrams because they don't understand them, because they can project things onto them.
We're overwhelmed by data right now, and need more tools to help us. This is a challenge for design. Even in this talk Mushon just narrated a series of Keynote slides. In visualization we have a distinction between explanatory and exploratory pictures. Exploratory visualizations are more challenging, when we are trying to find a story in the data. We have to go beyond nodes and edges… including flows, allowing comparison, and more. Mushon isn't offering a solution, but a path towards more best practices and tools for exploring this.
Another audience member notes a talk heard recently by someone trained in analysis, questioning the analysis of networks. That talk walked through a series of decisions along the way that she made which felt arbitrary. It asked, is that ok?
To the room of people here, how are people approaching network diagram analysis.
Ethan notes: Here at Civic Media we use networks to understand what influential sources are within a network of media. Our efforts to visualize flow include things like increasing the strength of a connection based on the number of links. This includes flow and directionality, which are often left out. But even with this, we have errors all the time - because people use links in different ways.
To address this, Civic uses language as an "implicit hyperlink" - where two media sources are connected if they use similar language. This is helpful because you get a visualization of how different sub networks are framing an issue. This is weird because sometimes it just doesn't work… we're not sure why. In addition, these maps are readable by experts only. One afternoon some Nigerian election experts came by, so he popped up a live graph about the recent election. This audience clustered around to dig into it. These are exploratory tools, but flawed. If you explain using then it denotes authority, which is worrisome.
Mushon wonders if we are using time. Ethan responds that we do time-slices, but don't animate. Mushon offers that using the time axis helps you read better.
Mushon has been running workshops on how to lie with dataviz. If you before you explain a network, you show a conspiracy theory within the network, this can serve as a cautionary tale. This prompts the idea that data work is about speech, not truth. This can help allies develop critical skills.
Erhardt shares Civic Media's analysis of the Trayvon Martin media coverage, which focused on the narrative of how the story changed over time. Validity is tied to the use and argument of our data analysis, not the analysis itself.
Another attendee mentions how the Guardian narrative became meaningful because it was easy to describe the relationships. A lot stories miss the material reality of the node. The people who could provide that context are often missing from the team analyzing the data.
Mushon agrees that these network visualizations are a huge abraction. You are trying to analyze a lot of data, but need to understand and research each of the nodes.
Mushon gave a similar talk at re:publica 2016
Note to the reader: This post will probably only be interesting for you if you're a facilitator or educator.
One of my driving goals in data literacy workshops I facilitate is to create space to play. I try to create that space by introducing fun materials, designing creative small group activities, introducing playful datasets, and more. But a recent workshop by Cédric Lombion from School of Data at the Data Literacy Conference got me wondering: am I leaving enough time to learn?
Cédric prompted those of us in the room to write down in stickies all the activities we do that are intended to improve data literacy. We then collaboratively grouped them on the wall. Nothing too surprising there, though I did learn about some cool activities that I should steal from Charles Népote of Fing and Samuel Huron. However, then we added a time axis, to note which activities were less than a day, which took a few days, and which were longer. The vast majority of activities we all did were less than one day.
The question for Cédric, and the rest of us, is whether that means we are doing a disservice to the learners we work with. Can we have real sustainable impact if we only string together short experiences into longer ones? Do we need to reconsider how we structure longer learning experiences to create more impact?
Here's the question this left me with - am I leaving enough time to fully flesh out these concepts in my workshops? Am I cramming too much in to short 3 hours sessions with learners? What would my activities look like if I left them with space to breathe? There are numerous pressures that push me to keep activities short in workshops settings:
- getting professionals to take time off from work for professional development is always hard
- "once a week" style programs have significant drop-off over time
- there's tons to cover when learning how to go from data to story, so it's hard to concentrate on just one piece.
So should I change my workshops to be more about going deep into one facet of data literacy? Hard to say. However Samuel and Pauline's "Let's Get Physical" talk the day before gave me an idea of what that might look like. Let's take the idea of "data sculptures" as an example. They have created a day long workshop, with great materials, prompts, and constraints, that lets participants really explore what it means to make data physical. It is exactly what my 5 minute data sculpture should be when it grows up!
Of course, I DO have one setting where I create lots of time to learn... the semester-long Data Storytelling Studio class I teach here at MIT. I do these quick activities with my students, but then they get a week to turn them into real projects.
The reflections from this great workshop make me think I should try a workshop for professionals that is focused on one piece of the data puzzle. I'm curious now - in your teaching are you leaving enough time to learn?educationliteracy
What is the state of the "empiricism agenda" to understand "what works" in policy? And what is it that we don't know?
I'm here at the What Works Global Summit (WWGS) in London, where David Halpern and Peter John are discussing the role of randomized trials in society. The WWGS is a gathering of practitioners in international development, policing, education, public health, activism, and many other areas where people have applied quantitative methods to get causal estimates on the outcomes of their social interventions.
The main speaker, David Halpern, is the Chief Executive at the Behavioral Insights Team, led the team since its creation, and before that was the chief analyst of the UK Prime Minister's Strategy Unit. David is also national advisor of the What Works Network, I've blogged about the the Behavioral Insights Team here before, sharing a talk by Oliver Hauser on the role of randomized trials in policymaking. Charing the conversation is Peter John, professor of political science and public policy at UCL, and lead author of a book (and article) called Nudge Nudge, Think Think: Two Strategies for Changing Civic Behavior (I summarize it here).
David starts out by referring to Archie Cochrane's book Efficiency and Effectiveness (1972), where he set out the argument for the use of randomized trials in medicine. In a side note, Cochrane asked:
what other profession encourages publications about its error, and experimental investigations into the effect of their actions? Which magistrate, judge, or headmaster has encouraged RCTs into their 'therapeutic' and 'deterrent' actions?.... Let us remember the number of bridges that have fallen down.
At the time, Cochrane was arguing for the basic randomized controlled trial. RCTs offer simple math for calculating potential outcomes, even as more causal methods are being available. In recent years, David has explained RCTs by describing how the British started to win in cycling. The British cycling team have worked on marginal gains: picking apart their work, testing different ideas, and making incremental improvements. If the UK could do that for cycling, why couldn't they do it for other areas of public interest? David describes this as "radical incrementalism." At the same time, it's also important to be able to make "leaps" -- describing Graeme Obree, whose crazy ideas about bicycle design allowed him to break the velodrome world speed record.
Next, David tells us about the journey from the "nudge unit" to the UK's more recent efforts with What Works centres. Back in 2010, the UK government created a "nudge unit," which aside from the policies it tested, has had a larger legacy to introduce the idea of empiricism into policy circles. To illustrate their work, David tells the story of an experiment the group did to test different kinds of tax letters to citizens. The group tested added extra lines to the letters, saying things like "Nine out of ten people pay their tax on time." By building different different kinds of interventions, they got effects of over five percentage points. Next, they asked about the effects among the people who were least likely to have an effect. By testing different messages with these groups, they were able to increase tax payment by seven percentage points, asking the question "what works for who?"
Why does this matter? These experiments tested an intervention that cost very little to try -- just changing the working of tax letters. So these experiments powerfully-demonstrated what governments could achieve by putting minimal effort into randomized trials. This has opened up opportunities for different kinds of experiments, like an experiment that tried to support learners to stay in further education colleges. They tried offering affirming values, saying something about grit, or texting student's friends to ask their friends how their learning was going. The intervention had a nearly six percentage point effect on school attendance, and they're waiting to see what the effect may be on scores.
The Behavioral Insights imported and exposed ministers to the idea of evidence based policy. The What Works movement is setting out to support people to generate new findings, transmit their findings, and support communities to adopt those findings. Together, they have created six "What Works Centres" in departments from health to policing, to support the UK government to evaluate social policy. Even in the health world, may issues of service delivery or public health have not be evaluated as well as pharmaceuticals. Davides arguesthat education randomized trials happened once a year at most, but thanks to the founding of the Education Endowment Foundation, they have funded 127 trials across 7,200 schools, creating resources for school administrators to make decisions based on the findings. The Early Intervention Foundation supports research on young people beyond school. Other groups include the What Works Crime Reduction, the centre focusing on Local Economic Growth, the Centre for Ageing Better, and the What Works Wellbeing centre. These centres are part of a larger network and wider work. David outlines the following goals:
- Stimulating ministerial interest. Rather than telling ministers they should hold off on an idea until there's an answer, we should tell ministers: you have more than one idea; why don't we implement the policy, try several things at once, and trim the one that is least effectively?
- (Re-)training the policy profession so policymakers are familiar with building policies that can be iterated and tested.
- The Trial Advisory Panel, which can offer feedback and advice on how to design trials
- Publishing what we don't know, but ought to. David wants governments to publish lists of things that we don't know that we ought to know the answer to.
- Moving from efficiency reviews to efficacy reviews in government.
In the next five years, David wants to live in a world where "radical incrementalism" is routine everywhere in government. He wants to see more work to identify what kinds of things work for what people. This will require governments to link people's data more closely. David wants to see empiricism applied to social work and the rest of the criminal justice system beyond police. Next, David wants to foster public demand and understanding. He wants people to interrogate the evidence behind different kinds of claims, and to expect that public services are taking an evidence-based approach. Finally, he wants to see the "What Works" enterprise grow internationally. David describes knowledge from experiments as a public good that we all contribute to every time we do an experiment.
David briefly notes that as we think about a society with more widespread experiments, it's important to take serious the public concerns about privacy and social control associated with experimentation.
David concludes by pointing to the "terra incognita" on a map in the early renaissance. He argues that by working together across countries and regions, we can support each other to fill in the gaps of experimental knowledge on the outcomes of policies.
Questions and Answers
Q: Where do experiments fit in the politics of mistrust, when many in the public may be less likely to care about evidence? David responds: Most ministers in government come in with strong beliefs, including policies. Some of them will not have a good evidence base. But there are so many choices in any given area. One approach is to ask what goals a person has where they haven't specified-- and then help them work on that. And then ask what other questions aren't headline news but which are amenable to evidence. The White House Social & Behavioral Sciences Team has published very dull things: things that everyone would agree to, and testing variations. If the UK nudge unit had started on radical reforms, they would have gotten knocked aside. There are million of other things to test that are less controversial. We can leave politicians their headline ideas. In some cases, policymakers have written "provisional" positions into legislation, budgeting resources to test them.
Observation: A participant from the Brookings Institution told a story from the White House Social and Behavioral Sciences Team. They've been running for two years, and many of their things have worked. He then talked excitedly about some of their failures. David responds that experimental groups have had many early successes because many government processes can easily be improved--anyone can do better. Over time, it will be important to develop good ways to talk about null results once the averages start to balance out.
Question: someone asks how to convince government to pay attention to evidence? David responds that with the What Works Centres, they have focused on building community among practitioners, who are able to carry out experiments independently of the national government. That community work can often create an appetite within government, says David.
Q: How do you think about ethics with policy trials? Halpern responds that he believes it's important to have an overt panel of the public involved in decisions about what the public thinks are acceptable policy experiments. If we want a more experimental government, we may need to turn to juries as a model for maintaining ethics in the public interest.david halpernpeter johnswhat works global summitwhat works centresrandomized trialsnudgepolicy evaluationfield experimentsvisualization
This is a liveblog of a talk at the 2016 Data Literacy Conference, hosted by Fing. This was liveblogged by Rahul Bhargava and Catherine D'Ignazio. These are our best attempt to record what the speak was talking about - any accuracy errors are our fault.Samuel Huron begins by talking about how we assume that data will be in visual form, on a screen. But there were many times where we generated lots of data, but we didn't use the same technologies and techniques. We used clay to represent numbers even before we had language. 8,000 BCE. That didn't have a separation between the recording device and the representation. Then the Sumerians started making marks, units, on a tablet. Suddenly they were comparing between lines and columns and such. Many more examples are on the DataPhys website. Then we abstracted to symbols, which let us compare things and look for patterns.
Anscombe's Quartet lets us see how different datasets can have the same algorithmically resolved properties. With our senses, we have some pre-cognitive operations that visualizations can take advantage of.
There is a separation between those that can work with the data, and those that cannot. He wasn't all be able to work with data and act in society using it. How can be welcome all to this? He looking into Constructive Visualization (paper). This study looked at a physical library of objects to build visuals that others could understand otherwise. THey created, updated, annotated a visualization using these basic building blocks. Some were duplications of existing shapes, but others were totally novel. Now there is an open source kit for doing this yourself.
Pauline Gourlet continues with a discussion of more than just the physical for representing data. Sometimes we talk about data we have, but other times we have to go and collect the data we need. What will the choice of the material we use induce in the story? How do we structure the data we are collecting?
Pauline's first example looked into looking at emotions and moods. A difficult thing to measure, capture, and report. They started with colors on fabric, because the way it spread on the fabric looked a bit diffused. So you could describe the mixture of emotions. Blue meant sadness, yellow was joy, etc. They animated the charts, and noticed dynamics and patterns in the data they captured.
The time of collection changed their moods (like art therapy)
The discussion it fueled changed how they talked about their emotions.
People wanted to explain the data, and projected themselves into the representations.
They repeated the whole project with primary school kids, to understand how they would experience it. They really wanted to do it. These kids who were 6 understand it, negotiated the meaning, and recontextualized it in the action they had just done. They did it again with graduate students in a design university. The process was a bit more systematized, with a star plot diagram with axis for things like stress. They 3d printed some of the resulting shapes. They also cut out the data as wooden pieces to see the stack of emotions over time. This let you separate and find families of similar days.
A separate example is Pauline's work looking into the usage of digital fabrication labs (FabLabs). They created a collaborative sculpture, where the placement of objects on a stick would encode what the users were doing. There was also a categorization of why people came to the FabLab (I experimented, I showed, etc). These mini sculptures encoded the timespan, regularity, and purpose of their visits to the FabLab.
Samuel speaks about a workshop they designed, to allow more open types of creation and physicalization. A workshop that is freeform, but lightly constrained. So there was a task that relied on different datasets. They wanted to explore how people would appropriate the physicalization. Pauline speaks to how they explored the physicalization. Sometime easy, involved, and simple to understand. They proposed basic tools and materials, selected materials. Wires, LEGO bricks, tokens, etc. 15 materials. They had 3 types of cards to start the activity - context cards, cards with datasets and cards with tasks (convince, discover, collect). Groups were invited to pick one card of each type, and get three materials. Then they presented to everyone (without describing it). The groups created physical manifestations of the data, liek a string that one would tie to a peg and pull to record the data. Another group created a participatory experience where you received cut outs of people representing asylum seekers, and placed them in your hands to force you to fit them in the EU somewhere.
Organizing on Social Media to Change Platform and Government Policies: Oxford Internet Politics and Policy Conference 2016
What role does social media play in supporting collective action, and how do people organize to change social media systems themselves?
I'm here in Oxford for the 2016 Internet Politics and Policy conference, hosted by the Oxford Internet Institute. Yesterday, I shared a paper on The Civic Labor of Online Moderators. Today, I was able to attend a fascinating session on the ways that people organize online for change.
Participatory Policymaking on Collaborative Social Media Platforms
Up first is Alissa Centivanny, a professor at the Western University, Ontario. In her talk on participatory policymaking on collaborative social media platforms, Alissa asked for suggestions and feedback on this work-in-progress research.
Platforms are becoming inseparable from many aspects of our lives, developing enormous power in our lives. They're often opaque, difficult to understand. As a society, we tend to see platforms as Godzillas: powerful entities that pop up from beneath the sea and unexpectedly carry out unfettered demonstrations of power. But all of us play a role in the power dynamics at play; how can we recognize that role?
One tragedy of the human condition is that each of us lives and dies with little hint of even the most profound transformations of our society and our species that play themselves out in some small part through our own existence.
James Beniger, from The Control Revolution
Alissa's goal today is to show how design, policy, and social practice co-evolve together. She cites Hood and Margett's "Tools of Government," Participatory Policymaking, Mechanic's "Sources of Power of Lower Participants" (1970), and Hirschman's "Exit, Voice, and Loyalty" (1970). She points out that while people have done huge amounts of work in participatory policymaking practice, those efforts have not often achieved substantial policy outcomes. Yet people like Mechanic have shown many ways in the everyday world that people with limited power do manage to influence those more powerful than them.
Alissa sets out to ask questions about how online platform users are involved in shaping the policy of online platforms. Her first example is the reddit blackout, a moment when moderators of thousands of subreddit communities took collective action against the platform, forcing it to change its practices and communities (Alissa has published research on the blackout, and so have I).
Alissa's second example is the controversy over a the Wikimedia Foundation's "Knowledge Engine," a proposed extension of the foundation's work that was controversial with wikipedia contributors and led to the resignation of the foundation's executive director. Unlike moderators in the reddit blackout, Wikipedians couldn't shut off parts of the site. Instead, says Alissa, they carried out high volume, detailed deliberative processes.
Alissa is still early in the research process and is still looking for resources, links, and people to interview.
Density Dependence (but not Resource Partitioning) on a Digital Mobilization Platform (Change.org)
Next up is Nathan TeBluthius, who shares work with Benjamin Mako Hill and Aaron Shaw (read the paper online here).
Online mobilization platforms have a problem of duplicate campaigns. Or is it a problem? Nathan shows us images from four campaigns to end dog meat festivals, only one of which was successful. Since there are only so many people, might these overlapping campaigns compete, detracting from the success of a campaign, or do they actually build and grow the movement overall? Your answer to this question determines on your view of the resources available to a movement.
To answer this question, Nathan scraped a dataset of petitions from Change.org. He then created clusters of petitions based on the similarity of the issues they take on. This allows Nathan to bring in theories theories of "density dependence" from ecology, which expect that clusters that are too small or too large will end up with less participation. In other words: highly unique campaigns will seem to niche to people (and not legitimate), and hugely popular campaigns will crowd each other out (through competition). His hypothesis is that the most successful campaigns will be part of mid-sized clusters. Nathan also mentions two other hypotheses.
In their statistical model, Nathan and his co-authors find support for this main hypothesis. Here's how they put it in the paper: "This curvilinear relationship between topic density and petition success suggests support for the idea that environmental pressures on petitions include both legitimacy and competition."
Tweeting for the Cause: Network Analysis of UK Petition Sharing
Peter Cihon, a gradstudent at Cambridge University, shares work he did with Taha Nasseri, Scott Hale, and Helen Margetts (paper here).
What is the relationship between social media and petition signatures? Peter looks at the UK's online petitions site during the period of the UK coalition government from April to June 2013. Past research has shown that the number of first-day signatures predict the success of a petition. In a time-shift analysis, Margetts and colleagues showed that the volume of tweets predicted the number of signatures. Yet in an analysis of German petitions, Lindner and Riehm showed that petitions increased inequality in political participation rather than broadening it (2011). Michael Strange's work has shown how activists form coalitions through petition creation.
Peter asks the questions: what does petition sharing actively look like, and who shares petitions? To ask this question, the team collected tweets from the Twitter search API from July 2013-March 2015 associated with 11,000 petitions. To study this, they researchers worked with two kinds of networks: petitions are connected to each other if the same user tweets about them. They also look at another network that connects users if they have shared the same petition. These are both implicit relationships based on activity.
Using this network, they used community detection algorithms to see if sharing yields topic clusters. They couldn't find evidence that users exclusively share a particular kind of cause. Next, they asked whether sharing tends to focus on popular or unpopular petitions. That was not the case; both successful and unsuccessful petitions are shared by the same users. Finally, they asked if centrally-shared petitions are associated with the number of petitions; there was no association between the centrality of sharing and the number of petitions.
What does this mean overall? Firstly, Twitter users share petitions of different topics and a wide range of outcomes, a finding that's similar to a study of power users on change.org by Huang et al on how activists are born and made. Second, central users in sharing networks are not formal interests, but acting as individuals. Finally, "latent" interest groups may be implied by similar behavior online. To end, Peter asks what might happen if people were made aware of each other?
Pascal Jurgens mentions: What is the scarce resource that's in play? A person might actually become excited to sign more petitions, but maybe their scarce resource is attention.
Helen Margetts has a conversation with Nathan. They note that Change.org does work to put people in touch with each other, using comments systems and recommendation systems to suggest other petitions that a person might choose to join. The site also sends people emails about similar petitions.
I asked Alissa about Hirschman's choices of exit, voice, and loyalty, which tend to be seen as individuals' choices, asking her how she thought about the collective action aspects of the reddit and wikimedia protests. Alissa brings a collective sensemaking method to try to understanding the collective understandings that emerge as people try to make sense of a larger distributed movement that is not coordinated or centralized. In contrast the petition sites try (unsuccessfully) to focus and limit the conversation to specific campaigns and counts. If one imposes structure that always stretches the boundaries and edges it introduces, the others begin more loose and may or may not approximate structure over time -- it's that collective sensemaking that Alissa studies.
This is a liveblog of a talk at the 2016 Data Literacy Conference, hoster by Fing. This was liveblogged by Rahul Bhargava and Catherine D'Ignazio. These are our best attempt to record what the speak was talking about - any accuracy errors are our fault.
Harshil Parikh is Co-Founder and CEO of Tuva. Tuva strives to empower various types of organizations to build a foundation in data and statistical literacy. The bad news is that defining data literacy is hard. It sits at the intersection of things like statistical literacy, visual storytelling, research methods and ethics/privacy concerns. It's hard to deliver on a product if you can't define it.
More bad news; everyone needs data literacy - Academia, Business and Government. But these different worlds have different ways of selecting, purchasing and using products and services. Even more bad news - what type of data literacy if useful to you depends on your sector.
But there is some good news! The need is being felt. Investment can be linked to outcomes and impact. There is opportunity for targeting offerings. These programs aren't zero-sum gains; the need continues over time.
Tuva started with building products for schools. They are in use by ~8500 schools, over 250 higher-ed institutions. Their business model is "freemium", where you can use some of it for free, and pay for more fleshed out offerings.
Tuva is focused now on building offerings for businesses. Any company that wants to stay competitive is investing in technology to support data. In addition, they are acquiring and retaining data talent. More importantly, they are realizing that these two things are not enough. This doesn't create a culture based on data and evidence. So they are investing in their existing workforce as well. This creates a shift in investment from just data producers to data consumers. They want to create a link between data and analytics.
This has lead Tuva to target specific audiences; specifically those that aren't very technical. This includes managers, team-leads, early-career professionals, and summer interns. They are building diagnostic assessments around literacy to justify expenditures.
Tuva has built a data visualization training for tax audit and advisory companies. They created a program for statistical literacy for a financial management company. Each of these requires them to be nimble in the approach and curriculum. They've rolled out multilingual trainings for the World Bank targeting internal and external partner audiences (ministries and CSOs).
Demand for data literacy is on the rise. These can be directly linked to organizational outcomes. Niche products can be produced for industry verticals. Data literacy is a fundamental skill in the 21st century. This is how to build a data literate future today.
This is a liveblog of a talk at the 2016 Data Literacy Conference, hoster by Fing. This was liveblogged by Rahul Bhargava and Catherine D'Ignazio. These are our best attempt to record what the speak was talking about - any accuracy errors are our fault.
Dirk Slater and Cédric Lombion are introducing School of Data by illustrating the challenges they face. It was originally launched in 2012, hosted out of the Open Knowledge Foundation, with idea that decentralized content would spread the idea around the world.
They realized several things: 1) online only was not enough. You need offline training as well. 2) Partnerships are necessary as well as translation into other languages if you have international aspirations. You have to go into the field and work with people who know the local context.
School of Data has members/partners, fellows, staff and a steering committee. They have a set of problems they have been working on.
First problem: People don't know how to work with data. He shows an example of Ximena Villagran, a fellow from Guatemala, who developed flashcards to teach people how to work with Excel pivot tables.
Second problem: People don't know how to run data projects. You have to have a methodology and a data mindset. For example, they have been working with Oxfam who has been pressuring governments to open data sets around French Banks' subsidiaries in tax havens. Oxfam staff had spent two months hand-entering the data which, with some training, could have been completed in a day. School of Data worked with them to show how to streamline these processes.
Problem 3: How do we scale this work? They only have a team of four staff members. They believe in face to face trainings, so they built and scaled a network. The people who are members are specialists in their audience and their context. They have people in the Philippines who are managing public resources but don't have access to computers and email. In Zambia people are working on health. It wouldn't be possible for one org to do this in one way, so working as a network is very important.
Problem 4: How do we share innovation? Sharing innovation is hard, because this involves significant documentation. Their summer camp helps start this, they have templates to support this, and the innovation fund that supports this via mini-grants. Their goal is to document so others can use this more effectively. The data viz card game is an example of this (based on the datavizcatalog site). Developing this further required support and funding.
Cédric hands over to Dirk to discuss how to improve their data literacy efforts.
Problem 5: How can the School of Data network improve data literacy efforts? They found that the School of Data curriculum is used by many to do their trainings. The "pipeline" is used by many outside of their network. The network has effectively become a community of practice.
Problem 6: How do we measure the impact of data literacy efforts? To do this they wanted to understand how the network practitioners understood data literacy. They got a huge range of responses from understanding a spreadsheet to using data how to solve problems. Dirk and Mariel standardized their definition to "The ability to apply and use information to make change." In order to measure impact they wanted to understand ways of doing social change and also understand who is doing the change (activists? CBOs? NGOs? governments?) For the network, this is really varied. We collectively need to be better at driving institutional change.
Problem 7: Can it be sustainable? The network is trying to understand how it can be self-sustaining for the long term. The NGOs in the network can monetize and productize their trainings for example with more of a "fee for service" model. They feel they need to move to make new partnerships with schools, civil society efforts, development initiatives, and the private sector. School of Data can help connect open data transparency efforts and citizens, for example.
Cedric wraps up by offering School of Data as a social and technical resource to the audience. They are trying to improve themselves as a platform to support their network of field operations. This contextualized work is molded as needed to suite the neets of each place.
Charles asks what their next challenges are. Cedric shares that the biggest one is spinning out of OKFN, and becoming completely financially independent.