Data, concepts and methods for large‐<i>n</i> comparative climate change adaptation policy research: A systematic literature review
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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