Systematic review approaches for climate change adaptation research
Bibliographic record
Abstract
Recent controversy has led to calls for increased standardization and transparency in the methods used to synthesize climate change research. Though these debates have focused largely on the biophysical dimensions of climate change, human dimensions research is equally in need of improved methodological approaches for research synthesis. Systematic review approaches, and more recently realist review methods, have been used within the health sciences for decades to guide research synthesis. Despite this, penetration of these approaches into the social and environmental sciences has been limited. Here, we present an analysis of approaches for systematic review and research synthesis and examine their applicability in an adaptation context. Customized review frameworks informed by systematic approaches to research synthesis provide a conceptually appropriate and practical opportunity for increasing methodological transparency and rigor in synthesizing and tracking adaptation research. This review highlights innovative applications of systematic approaches, with a focus on the unique challenges of integrating multiple data sources and formats in reviewing climate change adaptation policy and practice. We present guidelines, key considerations, and recommendations for systematic review in the social sciences in general and adaptation research in particular. We conclude by calling for increased conceptual and methodological development of systematic review approaches to address the methodological challenges of synthesizing and tracking adaptation to climate change.
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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 | 0.559 | 0.699 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.041 | 0.040 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".