Methods to Make Sense of Resilience: Lessons From Participant Coded Micronarratives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Okavango and Limpopo river basins are challenged by the effects of climate change, where communities that are traditionally dependent on natural resources for their livelihoods must adapt to conditions less predictable. Divergent interests among various stakeholders contribute to tensions between livelihoods and conservation, and understanding the perspectives of communities is critical for planning. However, traditional methodological tools are not adequate to reflect the diverse perspectives of respondents at scale. A baseline study of community resilience approaches to adapt to climate change across both river basin areas used a participant-coded micro-narrative approach to establish how people understand resilience across diverse areas. This methodological approach holds potential as a framework for understanding community experiences, but even methodologies designed for participation have limits in both processes and results. This article explores both and presents potential uses for participant-coded narratives in future evaluation processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it