Greener through gender: What climate mainstreaming can learn from gender mainstreaming
Bibliographic record
Abstract
Abstract Addressing the urgent global climate crisis demands a rapid and meaningful expansion of “climate mainstreaming,” which refers to the integration of climate objectives in all aspects of development programs and policies. However, progress remains slow and uneven due to bottlenecks in policy and institutional change. Considering the parallel struggle recorded over decades to mainstream gender across the same policy arenas, a key question emerges: what can climate mainstreaming learn from gender mainstreaming? To answer this question, we review 57 policy, strategy, and guidance documents of United Nations agencies, all of which integrate these themes into food security and broader development programming. Our analysis identifies gaps in climate mainstreaming efforts and derives lessons from gender mainstreaming to bridge these gaps. It underscores the importance of adapting programmatic mainstreaming strategies in response to evolving contexts, for example, by simultaneously considering both mainstreaming and targeted interventions. Additionally, it highlights the need to adopt organizational climate mainstreaming and establish mechanisms for accountability. Finally, it emphasizes the urgency of embracing a climate justice lens; in practice, this involves prioritizing populations at greater risk of climate change impacts and actively engaging diverse perspectives in decision‐making, particularly communities facing multiple forms of discrimination. This article is categorized under: Integrated Assessment of Climate Change > Assessing Climate Change in the Context of Other Issues Climate and Development > Sustainability and Human Well‐Being Policy and Governance > International Policy Framework
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".