Bibliographic record
Abstract
International environmental law-making (IEL) now increasingly highlights the importance of ensuring that women are enabled to play a key role in environmental management and decision-making at all scales, including in relation to the marine environment. This article examines narratives of women in international environmental law, with a focus on the marine environment and human rights intersections. This study reveals that there is a tendency to treat women both as victims in need of saving from ecological devastation, and as saviours whose empowerment will save the world. Recent developments at the intersection of human rights and the environment point clearly to the necessity of embracing an intersectional approach. Beyond this, it is necessary to reflect on what is meant by ‘women’ in international law to answer the question of whether greater inclusion of women in legal processes will make a difference to solving global and local ecological challenges. Ultimately, the article argues that meaningful action will not happen until affluent and powerful men and women learn how to embody the idea of woman themselves, rather than placing the burden to save the world on those whose vulnerability is worsened if not created by affluent overconsumption.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".