Mobile Adaptation and Sticky Experiments: Circulating Best Practices and Lessons Learned in Climate Change Adaptation
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
Abstract This article engages with the recent geographical literature on policy mobilities in order to examine how the World Bank mobilizes climate change adaptation 'best practices'. Drawing from the relational case study of the Kiribati Adaptation Project and the Community Resilience to Climate Change and Disaster Risk in Solomon Islands Project, the article explores the complex and intensive work required for mobilizing lessons and practices. The analytical work required in building the Kiribati Adaptation Project as a World Bank success story and policy model worthy of replication in new sites is demonstrated. However, heeding calls within the policy mobilities literature to avoid fetishizing mobility and attending to the contradictions between global flows and local institutional specificity, the article finds limited evidence of replication in noted sites of emulation. Instead, there is compulsive citation, publication, and circulation of experiences and successes within the World Bank, which operates to build internal and external legitimacy.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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