Africa is BIG! And yet so many foreign NGOs claim to help “Africa.”
BatchGeo - Stories collected in East Africa
Why do this?
Existing Feedback loops don’t solve problems
When many scribes collect stories about many NGOs, we can geographically connect them.
When we presented their own perspective back to themselves, they felt it was very incomplete, but were willing to accept the storyteller’s map more readily. No human rights / transparency NGOs named. Lesson: NGOs’ own perspective is that funding partners are worth mentioning / thinking about, but working / advocacy partners are not.
10/25/11 Stories in action (www.GlobalGiving.org/stories)
24,400 stories collected in 2011 >500 locations Stories about “community efforts” http://batchgeo.com/map/350267f319cf19cedfdf447fc0afa5f8 (colors are arbitrary --- distance from Nairobi)
<ul><li>our GG Storytelling process… </li></ul><ul><li>Build a network of NGOs --- took us 5 years </li></ul><ul><li>Invite partners to find a handful of local young people who want to become story collectors --- a dozen scribes / town </li></ul><ul><li>Visit and train groups of scribes across the region --- over 2000 people in 2011 </li></ul><ul><li>Collect stories on paper monthly, transcribe to web </li></ul><ul><li>Analyze stories for patterns, lessons, & overall messages --- SenseMaker® and other visual tools </li></ul><ul><li>Improve story quality though feedback and meta-analysis </li></ul><ul><li>Regularly deliver feedback to NGOs --- community feedback sessions every 3-6 months </li></ul><ul><li>SMS feedback & news to storytellers --- just starting </li></ul><ul><li>Meta-Analyze all of the above in order to learn about our network, and promote organizations with high curiosity --- prerequisite to problem solving & innovation </li></ul>
Community feedback reaches donors and local organizations Might better align projects with needs Incentive: Easier Evaluations? $300 billion /year in P2P aid Technology-aided feedback loops
Existing aid feedback loops Policy oriented Slow to adapt Local people not involved Incentive: Helping donor countries’ economies $127 billion / year in ODA
Nuts & bolts of the method Paper collection <ul><li>Training </li></ul><ul><li>collect 20-30 / month --- get 12 cents per story </li></ul><ul><li>get 2 stories per person --- for a “within subjects” baseline </li></ul><ul><li>start with people you know / comfortable talking to </li></ul>
More examples on my blog: chewychunks.wordpress.com start end
Analysis tools Comparing patterns in groups of stories Geo Mapping Face to face meetings Seeing story themes For comparing interpretations and getting a reality check SenseMaker® Gephi Mapping relationships Story search & download Community of 400 NGOs in 3 clusters FGM
SenseMaker® versus other methods Requires: Lots of narrative fragments Signification framework ( questionnaire about the story told ) (quasi) experimental methods narratives (case studies, MSC) SenseMaker® based 1. O utputs answer about which intervention changed which variables most in a particular context in-depth experiences that explains a change process how different people experience change process; type of changes /behaviours/ values 2. T ype of study and frequency one-off comparison; usually no intermediate data points process analysis; one-off study or ongoing one-off study or ongoing monitoring of emerging patterns (with feedback loops) 3. O rganising principle for question focus comparing specific interventions, anticipated observable change variables – before/after and with/without change process, context, specific changes and their value (not pre-determined) values, behaviours, beliefs that are the focus of change 4 T ype of data on which analysis is based quantitative variables that either count or are relative score (0 to 10); sometimes qualitative studies to explain why selection of in-depth experiences in context; usually no quantitative comparisons quantified narratives from people (nuanced knowledge); context provides meaning; numbers enable seeing of trends 5 N umbers summaries people’s opinions or measurable variables; strong focus on average effect; no focus on context-specific insights no averaging; few if any quantities; sometimes limited cases assumed representative identifies emerging patterns based on fragments of people’s experiences; moving between numbers and stories gives contextualised statistics 6. R igour defined by statistically validated causal attribution; counterfactual quality of in-depth study; probing; explaining diversity and number of stories; ability to infer from nuanced analysis; utility of patterns for action A Aggregation easy via standardised responses rare as low ‘n’ to aggregate; very time-consuming, external interpretation easy through relative positioning on triads/dyads
Examples of analysis Root causes of a complex social problem ( drilling down ) Looking at 1617 “school fees” stories: Those tagged with “need” + “failure” are coming from women. From 1784 “hiv/aids” stories: Those tagged “security” + “family” and not about any organization are about rape or sexual assault, mostly from women. Licensed SenseMaker® software
Examples of analysis Root causes of a complex social problem ( rape ) Mrembo girls talk about… Sita Kimya men talk about… Comparing story sets reveals different emphasis
Examples of analysis Reveal hidden / unconscious biases among storytellers Licensed SenseMaker® software Kenya Uganda
Examples of analysis What are people talking about in a community? Stretching SenseMaker® to visualize story characteristics Licensed SenseMaker® software westandwiththe99percent.tumblr.com
Examples of analysis What are people talking about in a community ( phrases )? Network diagrams are generated with python / networkx and visualized with Gephi (all free software)
Examples of analysis What are people talking about in a community? http://www.globalgivingcommunity.com/circle_4.php
Examples of analysis Who is / ought to be working with whom? Network diagrams are generated with python / networkx and visualized with Gephi (all free software) Full NGO network derived from stories core NGOs
Examples of analysis Who is / ought to be working with whom? Organizations’ perspective generated during NGO meetings
Our world is full of complex problems… We need non-linear visualization techniques to understand them.