Methods to Make Sense of Resilience: Lessons From Participant Coded Micronarratives
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".