Women and biodiversity: The long journey from users to policy‐makers
Bibliographic record
Abstract
Abstract Although there has been a broad acknowledgment that women's local and traditional knowledge is fundamental to guarantee food security and conserve biological diversity, few women are represented at the managerial and decision‐making level of environmental movements and organizations. The United Nations, its agencies and agreements have long promoted the full and effective participation of women in decision‐making processes. So how can commitments contained in international agreements be translated into concrete actions? By using the case of the Convention on Biological Diversity, one of the key agreements adopted at the 1992 Earth Summit in Rio de Janeiro, this article analyses how gender‐equitable initiatives tend to assume an ad hoc character with few governments effectively involving women in their sustainable development strategies. The views expressed in this article are those of the authors and do not necessarily reflect the official position of the United Nations or its subsidiary bodies.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".