L’harmonisation des mesures d’adaptation au changement climatique dans la planification territoriale
Bibliographic record
Abstract
Les catastrophes naturelles et leurs conséquences, couplées à la vulnérabilité des populations et auxproblématiques sociales, ont provoqué des réformes politiques, sociales, économiques et environnementales dans plusieurs régions du monde. Ces évènements ont des impacts sur le développement des sociétés et ont entraîné un changement de paradigme dans la vision du développement des territoires. À cet effet, la planification territoriale ne se limite plus aux aspects socioéconomiques, mais aussi aux aspects environnementaux et éthiques intégrés au concept de développement durable. Cependant, les mesures d’adaptation à ces problématiques causées par le changement climatique mondial ne sont pas réellement prises en compte dans les plans de développement des territoires, ce qui met en évidence un problème d’harmonisation des mesures, sur lequel portera notre analyse. Natural disasters and their consequences, along with the vulnerability of populations and social issues, have led to political, social, economic and environmental reforms in numerous areas of the world. These events had impacts on the development of societies and led to a paradigm shift in the vision of territorial development. Land use planning is therefore no longer limited to socio-economic aspects, but also to environmental and ethical aspects that are integrated into the sustainable development concept. However, the adaptation measures taken against problems caused by global climate change are not actually taken into account in territorial development plans. This highlights a problem of measure harmonization, that our analysis focuses on.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".