Interlocal adaptations to climate in east and southeast Asia: sharing lessons of agriculture, disaster risk reduction, and resource management
Bibliographic record
Abstract
A quick skim through the list of 35 contributors in Interlocal adaptations to climate change in east and southeast Asia makes it clear that this edited volume is not a ‘business as usual’ global environmental governance book. Typically, the latter are somewhat limited in their approach, providing a comparative analysis within global governance frameworks. In contrast, this ambitious book opens with a call for ‘a participatory approach that includes a wide variety of local stakeholders for identifying problems, planning adaptation strategies, and implementing their options’ (p. vi). The editors achieve their intended purpose with the diversity of institutions, regional expertise and methodological approaches represented. Overall, this edited volume offers a rich and technical exploration of the case-studies on how to build knowledge around climate adaptation policies in east and south-east Asia. The editors and contributors do not try to fit the unique adaptation contexts of the countries examined into a general framework. Rather, there is a genuine attempt to understand adaptation within each context and to draw actionable lessons that can contribute to regional knowledge development and to practical results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".