Politiques urbaines et biodiversité en ville : un front écologique? Le cas de la MGP, Métropole du Grand Paris
Bibliographic record
Abstract
Au sein de la plupart des grandes métropoles, les pouvoirs publics développent des stratégies en faveur de la biodiversité, en vue de renforcer les espaces de nature dans le tissu urbain ou de renaturer des espaces artificiels. La MGP, Métropole du Grand Paris, créée en 2016, rassemble la commune parisienne et plus de 130 autres communes dans un établissement territorial chargé de la planification urbaine. L’analyse des entretiens effectués auprès de 21 élus de la MGP permet de suggérer que la biodiversité constitue un levier favorable à l’épanouissement de ce Grand Paris sous la forme d’un double front écologique. La biodiversité fait l’objet d’une attention particulière du fait d’une reconnaissance partagée de ses services écosystémiques et les politiques publiques intègrent la biodiversité comme élément structurant dans les projets de rénovation urbaine au sein de la MGP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".