Data, concepts and methods for large‐<i>n</i> comparative climate change adaptation policy research: A systematic literature review
Bibliographic record
Abstract
Climate change adaptation research is dominated by in‐depth, qualitative, single‐ or small‐n case studies that have resulted in rich and in‐depth understanding on adaptation processes and decision making in specific locations. Recently, the number of comparative adaptation policy cases has increased, focusing on examining, describing, and/or explaining how countries, regions, and vulnerable groups are adapting across a larger sample of contexts and over time. There are, however, critical empirical, conceptual and methodological choices and challenges for comparative adaptation research. This article systematically captures and assesses the current state of larger‐n (n ≥ 20 cases) comparative adaptation policy literature. We systematically analyze 72 peer‐reviewed articles to identify the key choices and challenges authors face when conducting their research. We find among others that almost all studies use nonprobability sampling methods, few existing comparative adaptation datasets exist, most studies use easy accessible data which might not be most appropriate for the research question, many struggle to disentangle rhetoric from reality in adaptation, and very few studies engage in critical reflection of their conceptual, data and methodological choices and the implications for their findings. We conclude that efforts to increase data availability and use of more rigorous methodologies are necessary to advance comparative adaptation research. This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
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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 | 0.177 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.054 | 0.048 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".