Stuck in its straight jacket. How legal reasoning eludes intersectionality in the climate-health crisis
Bibliographic record
Abstract
The current single-axis approach to climate change marginalizes individuals and groups who are burdened by multiple layers of gender, status, disability, territorial or geographical discrimination. This contribution argues that intersectional aspects of climate and health inequalities must be acknowledged within legal reasoning. Through a multi-case study among climate change litigation, the paper scrutinizes and retraces intersectionality all along the judicial process, from lower to higher courts of United States, Switzerland, Colombia, and Canada. The illustrative analysis shows how intersectionality hardly enters the legal realm even though the global and social crises urgently require a new intellectual paradigm. Combining a feminist approach and legal expertise, it addresses how and questions why forty years of scholarship on intersectionality have not resulted in its absorption into judicial practices. The paper contributes to a broader claim that power relations must be recognized to address climate and health issues meaningfully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".