Our goal is to invent a way to move from stories to graphs and back again to that the limitations of stories and graphs can be cover come by combining the strengths of both in an iterative process. This is meant to be used by regular folk as then deal with neighborhood-level systems.
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Federated Wiki pages: are themselves graph nodes, whilst the nodes are described by their JSON property map.
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# Below: work by Marc Pierson and Kerry Turner in About Kerry.
# Sequence
**Situation / Experience**
**Understanding / Sensemaking**
**Narrative / Story** * This is where we enter our work flow. * We are collecting compact stories for this process.
**Transcribe / Text** * We are using audio or video files. * We use Descript to create rough transcriptions. * We clean them up.
**ID Key Variables** * Kerry identifies a selected set key variables. * Marc IDs variables. * Others my do so too. * We have the original story teller check the variables for relevance and completeness when feasible.
**Causal Loop Diagram** * Kerry creates a CLD in Vensim
**`Driver Diagram** * Marc and Kerry create driver diagrams from the CLD.
**Models**
**Reflection** * The models are presented to various people familiar with the experiences and or the narratives. * We collect their reactions and insights to the interplay of model and story.
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We will learn and refine our story-to-model approach to discover how to best use it in local settings.