Strategies for gender mainstreaming in climate finance mobilisation in southern Africa
Bibliographic record
Abstract
This study examines the practice of gender mainstreaming in the context of climate finance mobilisation. It reveals how financial institutions are adopting shifts to organisational strategy, policy, and practice that advance the integration of key aspects of social sciences. This article specifically examines the role played by the Green Climate Fund’s Gender Policy in promoting a shift in the organisational strategies developed by development finance institutions and commercial banks in southern Africa. It reveals how practitioners are grappling with the evolving role of financial intermediaries in promoting a shift towards low-emissions, climate-resilient, and just development. The analysis uncovers foundational components, highlights key lessons, and identifies strategic approaches to institutionalising gender mainstreaming practices. Critically, the research reveals that whilst gender mainstreaming involves multiple practicalities, the financial institutions that have most extensively institutionalised gender mainstreaming practices have done so by recognising its normative basis and have perpetuated changes to organisational values and culture alongside more pedestrian policy amendments. One of the critical aspects of this culture shift is the recognition that transformative social impacts in climate finance are predicated on the design and implementation of projects that account for existing gender-based vulnerabilities whilst also identifying and maximising opportunities for all genders. The study builds on and contributes new knowledge to existing frameworks for understanding gender mainstreaming in relation to multilateral climate finance.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".