Bibliographic record
Abstract
Le changement climatique anthropique pose un défi mondial important en raison de l’augmentation des émissions de gaz à effet de serre. Il engendre également des perturbations juridiques, car il soulève des questions juridiques difficiles à traiter par les tribunaux et les doctrines juridiques existantes. Cette thèse se concentre sur les défis juridiques auxquels sont confrontés les tribunaux, notamment en ce qui concerne la justiciabilité du changement climatique, la détermination et l’étendue des obligations gouvernementales et l’établissement de la causalité juridique dans les litiges liés au changement climatique. À travers une analyse détaillée de cas emblématiques tels que Urgenda aux Pays-Bas; ENJEU, La Rose, et Mathur au Canada; Neubauer en Allemagne; et KlimaSeniorinnen devant la Cour européenne des droits de l’homme, cette thèse illustre le rôle que jouent les traditions de droit civil, de common law, et de droit européen dans la manière dont les tribunaux abordent ces questions. Cette comparaison met en évidence les approches et contributions distinctes de chaque tradition, ainsi que les façons dont elles peuvent converger
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".