Empirical assessment of equity and justice in climate adaptation literature: a systematic map
Bibliographic record
Abstract
Abstract The normative concepts of equity and justice are rising narratives within global climate change discourse. Despite growing considerations of climate equity and justice within the adaptation literature, the extent to which adaptation research has worked to empirically assess and operationalize concepts of equity and justice in practice remains unclear. We employ a systematic mapping approach to examine how equity and justice are defined and understood within empirical climate change adaptation research, and how extensively they are being assessed within adaptation literature. Structuring our work using a conceptual approach focusing on distributional, recognition, procedural, and capability approaches to justice, we document and review articles that included empirical assessments from searches performed in Web of Science™, Scopus®, and Google Scholar™ databases. Our results highlight that greater attention in the literature is given to certain aspects of justice (e.g. distributive and procedural justice concerns) on certain topics such as climate policy and adaptation finance. Most of the included papers scored highly according to our criteria on their empirical assessment of equity and justice. The lowest scores were found for the methodological rigor of assessments. We find limited research on empirical equity and justice assessment and call for a multiscale and holistic approach to justice to address this research gap.
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.
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 | 0.064 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.078 | 0.072 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".