Idea webinar-oct-25-2011


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  • Africa is BIG! And yet so many foreign NGOs claim to help “Africa.”
  • BatchGeo - Stories collected in East Africa
  • Storytelling process
  • 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.
  • Idea webinar-oct-25-2011

    1. 1. 10/25/11 Stories in action (
    2. 4. 24,400 stories collected in 2011 >500 locations Stories about “community efforts” (colors are arbitrary --- distance from Nairobi)
    3. 5. <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>
    4. 6. 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
    5. 7. Existing aid feedback loops Policy oriented Slow to adapt Local people not involved Incentive: Helping donor countries’ economies $127 billion / year in ODA
    6. 8. 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>
    7. 9. More examples on my blog: start end
    8. 10. 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
    9. 11. 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
    10. 12. 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
    11. 13. 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
    12. 14. Examples of analysis Reveal hidden / unconscious biases among storytellers Licensed SenseMaker® software Kenya Uganda
    13. 15. Examples of analysis What are people talking about in a community? Stretching SenseMaker® to visualize story characteristics Licensed SenseMaker® software
    14. 16. 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)
    15. 17. Examples of analysis What are people talking about in a community?
    16. 18. 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
    17. 19. Examples of analysis Who is / ought to be working with whom? Organizations’ perspective generated during NGO meetings
    18. 20. Our world is full of complex problems… We need non-linear visualization techniques to understand them.