Local knowledge in climate adaptation research: moving knowledge frameworks from extraction to co‐production
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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