Charting knowledge co‐production pathways in climate and development
Bibliographic record
Abstract
Abstract Climate change poses significant global challenges. Solutions require new ways of working, thinking, and acting. Knowledge co‐production is often cited as one of the innovations needed for navigating the complexity of climate change challenges, yet how to best approach co‐production processes remains unclear. In this article, we explore the ways in which climate and development researchers are approaching the co‐production of knowledge and grapple with the extent to which the modalities used are reaching their stated potential. Using a multiple case analysis of six examples of successful co‐production, we outline a spectrum of co‐production approaches and outcomes and examine the drivers and challenges to co‐production in practice. Drawing on the case evidence and literature, we propose a heuristic that maps out this spectrum of aims and approaches to co‐production and that could inform reflections on how those planning co‐production processes envision them in 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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".