Download - Idea webinar-oct-25-2011
04/07/23 1
Stories in action(www.GlobalGiving.org/stories)
24,400 stories collected in 2011>500 locationsStories about “community efforts”http://batchgeo.com/map/350267f319cf19cedfdf447fc0afa5f8(colors are arbitrary --- distance from Nairobi)
our GG Storytelling process…
1. Build a network of NGOs --- took us 5 years2. Invite partners to find a handful of local young people who
want to become story collectors --- a dozen scribes / town3. Visit and train groups of scribes across the region --- over
2000 people in 20114. Collect stories on paper monthly, transcribe to web5. Analyze stories for patterns, lessons, & overall messages ---
SenseMaker® and other visual tools6. Improve story quality though feedback and meta-analysis7. Regularly deliver feedback to NGOs --- community feedback
sessions every 3-6 months8. SMS feedback & news to storytellers --- just starting9. Meta-Analyze all of the above in order to learn about our
network, and promote organizations with high curiosity --- prerequisite to problem solving & innovation
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 loopsPolicy 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
Training• collect 20-30 / month --- get 12 cents per story• get 2 stories per person --- for a “within subjects” baseline• start with people you know / comfortable talking to
start
end
More examples on my blog:chewychunks.wordpress.com
Analysis toolsComparing patterns in groups of stories
Geo Mapping
Community of 400 NGOs in 3 clusters
Face to face meetings
F GM
Seeing story themes
For comparing interpretations and getting a reality check
SenseMaker® GephiMapping relationships
Story search & download
SenseMaker®versus other methods
Requires:Lots of narrative fragmentsSignification framework (questionnaire about the story told)
(quasi) experimental methods narratives (case studies, MSC) SenseMaker® based
1. Outputs 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. Type 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. Organising 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 Type 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 Numbers 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. Rigour 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 analysisRoot 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 analysisRoot causes of a complex social problem (rape)
Mrembo girls talk about… Sita Kimya men talk about…Comparing story sets reveals different emphasis
Examples of analysisReveal hidden / unconscious biases among storytellers
Licensed SenseMaker® software
Kenya Uganda
Examples of analysisWhat are people talking about in a community?Stretching SenseMaker® to visualize story characteristics
Licensed SenseMaker® software
westandwiththe99percent.tumblr.com
Examples of analysisWhat are people talking about in a community (phrases)?
Network diagrams are generated with python / networkx and visualized with Gephi (all free software)
Examples of analysisWhat are people talking about in a community?
http://www.globalgivingcommunity.com/circle_4.php
Examples of analysisWho 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 analysisWho 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.