Local knowledge in climate adaptation research: moving knowledge frameworks from extraction to co‐production
Bibliographic record
Abstract
This review consists of a systematic assessment of climate change adaptation literature to elicit major trends, discourses, and patterns in how local knowledge is conceived. We report on conceptual and geographic trends within the literature, including the practice of assessing local knowledge against scientific benchmarks, and present results of a textual network analysis that illustrates overlap and co‐occurrence among different characterizations of local knowledge. In critically assessing the dominant trends we draw special attention to problems associated with the extraction of local knowledge without due consideration of how this process is embedded and inextricable from local contexts and sociotechnical orders. Drawing on theories of science and technology that examine the ontological politics of research practices, we propose a co‐productive path forward for local knowledge mobilization to inform adaptation decision‐making, which we argue facilitates the transformation of the institutional and governance arrangement of climate adaptation to provide greater flexibility and experimentalism in research and decision‐making. WIREs Clim Change 2017, 8:e475. doi: 10.1002/wcc.475 This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice
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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 | 0.080 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".