Locally led adaptation: Promise, pitfalls, and possibilities
Bibliographic record
Abstract
Locally led adaptation (LLA) has recently gained importance against top-down planning practices that often exclude the lived realities and priorities of local communities and create injustices at the local level. The promise of LLA is that adaptation would be defined, prioritised, designed, monitored, and evaluated by local communities themselves, enabling a shift in power to local stakeholders, resulting in more effective adaptation interventions. Critical reflections on the intersections of power and justice in LLA are, however, lacking. This article offers a nuanced understanding of the power and justice considerations required to make LLA useful for local communities and institutions, and to resolve the tensions between LLA and other development priorities. It also contributes to a further refinement of LLA methodologies and practices to better realise its promises. Ultimately, we argue that the utility of the LLA framing in promoting climate justice and empowering local actors needs to be tested empirically.
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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 | 0.082 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".