A synthesis of convergent reflections, tensions and silences in linking gender and global environmental change research
Bibliographic record
Abstract
This synthesis article joins the authors of the special issue "Gender perspectives in resilience, vulnerability and adaptation to global environmental change" in a common reflective dialogue about the main contributions of their papers. In sum, here we reflect on links between gender and feminist approaches to research in adaptation and resilience in global environmental change (GEC). The main theoretical contributions of this special issue are threefold: emphasizing the relevance of power relations in feminist political ecology, bringing the livelihood and intersectionality approaches into GEC, and linking resilience theories and critical feminist research. Empirical insights on key debates in GEC studies are also highlighted from the nine cases analysed, from Europe, the Americas, Asia, Africa and the Pacific. Further, the special issue also contributes to broaden the gender approach in adaptation to GEC by incorporating research sites in the Global North alongside sites from the Global South. This paper examines and compares the main approaches adopted (e.g. qualitative or mixed methods) and the methodological challenges that derive from intersectional perspectives. Finally, key messages for policy agendas and further research are drawn from the common reflection.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".