Les politiques publiques d’équipements sportifs vues à travers quatre villes françaises de 1960 à nos jours ; N° spécial Aménagement et planification territoriale des installations sportives et de loisir: de l'équipement de proximité à l'infrastructure d'
Bibliographic record
Abstract
In France, the local planning of sports facilities originates from successive public policies carried out at various territorial levels, from national to local. Changes in the stock of sports facilities in four towns, including low-income areas, are analyzed based on the different national facilities plans of the 1960s, the effects of decentralization since the 1980s, and the evolution of urban policies since the 1990s. The uneven impact of these various periods of public policies can be observed at a local level. In addition, the authors study the different approaches to financing adopted by municipalities with distinct profiles but with characteristics which can be transposed to medium-sized towns. While it is usually assumed that national policies in this field have a homogeneous impact nationwide, a cartographic study that accounts for the local context clearly shows the opposite. This study evidences the fact that national policies can have meaningful results only when there is considerable financial assistance provided and when the local authorities are involved. Municipalities remain key actors in the development of infrastructures, which is why there are so many discrepancies between them.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".