Integrating power, justice and reflexivity into transformative climate change adaptation
Bibliographic record
Abstract
• Provides strong normative grounding for the idea and practice of transformative adaptation. • Contends that transformative adaptation requires transformation among those who implement interventions. • Presents four vectors of transformative adaptation as a reflexive form of organisational learning. • Highlights the importance of power, knowledge, coalitions and tradeoffs within programming. • Provides a clear guide to existing literature on transformative adaptation. Transformative adaptation requires transformation among those who fund, plan, implement and evaluate interventions. In response, we emphasise the need for donor and implementing organisations to self-reform to create the necessary space and support for adaptation projects that embrace a transformative ethos. We argue that projects can appropriately centre justice as the primary goal of transformative adaptation by (1) confronting power relations, (2) embracing knowledge pluralism, (3) fostering bottom-up coalitions, and (4) recognizing trade-offs and unexpected outcomes. At the heart of this reflexive approach is the foregrounding of learning processes targeted towards shifting knowledge and power that is critical to avoid adaptive outcomes that exacerbate the vulnerability and exclusion of already marginalised groups.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.128 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".