Systematic review approaches for climate change adaptation research
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it