Sensemaker for Partos Plaza - Irene Guyt

Making Sense of Sensemaker ® : Evaluating development initiatives through micro-narrative capture and self-tagging in Kenya Partos Plaza October 14, 2010 Dr. Irene Guijt, Learning by Design with John Hecklinger and Marc Maxson (GlobalGiving)

Connecting Needs Chronologically
Learning by Design
find a methodological option for ‘seeing’ emergence
Cognitive Edge
test application in international development
Rockefeller Foundation
fund methodological innovation
GlobalGiving
generate non-intermediated target group feedback flows

Options to Find and Express
Statistics
Decontextualised fragments (end results/inputs)
Outliers ignored
Hard to get to explanatory level
Work well within the known/knowable
Stories
Few in-depth voices, sometimes purposively selected (dilution)
Biases of the intermediary
Representativeness assumed; validity questioned
Richness cannot be dealt with if many stories

But when you’re off the beaten path…
future doesn’t necessarily mirror the past
ongoing sensing of multiple perspectives needed
real time feedback loops
Stories, then Statistics in SenseMaker ®

GlobalGiving’s M&E
self-generated reports
incidental evaluators
postcards from the field
But
closer to users
continuous
not mediated

Design Features
Numbers derive meaning from their context
Stories told by people are filters through which they make decisions
Mass capture of fragmented material, self signified at the point of origin gives quantitative data
Semi-constrained signifiers
Visualization most important human sense-making capability (patterns, then stories)

Methodological Logic
Prompting question or image
triggers a lived experience
Storyteller self-indexes story
gives meaning, but can be done by others
Software detect patterns
visual patterns among stories
Sense-making in dialogue
people discuss patterns and story clusters
Act on patterns
Weak signals phase shifts
Stimulate beneficial patterns; dampen undesirable ones

Designing Use of SenseMaker ®
Develop signification framework
Content and ‘translation’
Strategies for data collection
Who, how, how often, how long, etc?
Data analysis
Where to nest learning?
How will analysis take place, who will be involved, what is the rhythm of analysis (how often at which level?)
Feedback loops with the system
Who needs to look at what aspects of the initial analysis and how, in order to enable change?

1a. Triggering stories

Example of a Story (GlobalGiving data)
This library is located in Kibera Otiende opposite the upgrading project. It started some years back not more than three years. Their library came because most of children in the community lacked textbooks. They demanded that in school everybody is entitled to to homework and in order the homework to be done and saw the way forward is to contribute together and share the problem equally and raise some money for the library construction then for the books. They struggled to raise the money and at long last they raised and built a well equipped nursery textbooks because the of the victims were primarians. Now the children are happy and also the load that parents had is reduced. The community is very proud because their children are passing and doing well during their national exams. That is a sign of excel to parents and also children since they are making good use of the library and materials available. The library is there upto date and still going strong.

Insert short Rimisp story

1b. Questions to self-index
Storyteller gives meaning to own story
Triads
Polarities
Multiple options
‘ About you’

The questions
Probes for additional story context: 9 questions about each story; 5 questions about storyteller
This community effort improved … Social relations Physical conditions Economic opportunities

2. Strategies for Data Collection
paper-based, then typed up
3000+ stories (2700 usable) in 10 weeks
via 50 volunteers (snowball) for 1500$
Incentives matter, so does chasing
Target a focused time to trigger flood of stories

3. Data Analysis
Any combination of triad, polarity, MCQ, demographics
Explorer: glance, browse, compare, range, distribute, cluster, graph, landscape

Stories (n = 2530)
#1 Food
#2 Shelter
#3 Safety
#4 Water
#5 HIV/AIDS
#6 Education
Experts/implementers (n=65) Why self-signify?

4. Feedback loops
Dissemination and institutional feedback
Partnerships
Community feedback
Geo-tagging
Global Giving

Next Steps for GG
Improving story collection: signification framework & data uploading
Experiment with different incentives
Expand geographic scope
Integrated technology - combined web-SMS platform
Cross-organization learning
Use ‘needs’ stories for advocacy
Partner rewards program

Applications
Organizational aggregation from diversity
Rolling baselines
Cross-organizational comparisons
Focused moments – MTRs, final evaluations

More information
http://www.cognitive-edge.com/blogs/news/2010/09/final_report_published_by_glob.php
http://www.youtube.com/watch?v=ZV-lcTuUawc&NR=1
http://www.sensemaker-suite.com/sensemaker-videos.html

Sensemaker for Partos Plaza - Irene Guyt

by annepartos on Oct 29, 2010

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Sensemaker for Partos Plaza - Irene Guyt — Presentation Transcript

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