L’événementiel sportif municipal au secours des éléphants blancs : l’exemple de Charléty sur Neige
Bibliographic record
Abstract
While most impact studies focus on competitive sports events, just a few pay attention to municipal sports events. Nevertheless, politicians use them to attain the purpose of a public policy: either to improve a situation, or to fill a gap. This is the case of ‘Charléty sur Neige,’ which aims to address the underutilization of Charléty stadium (20,000 seats) in the 13th arrondissement of Paris. This free event transforms the facility into a ski resort for youths aged between 3 and 16 years. This article analyzes the various impacts of the event in order to identify the fit between the objectives and the effects observed. On the one hand, the event becomes a way to respond to cross-cutting issues far beyond the initial objective of the facility’s animations. On the other hand, the stakeholders’ interactions and local configurations reveal missed opportunities, confirming the essentially non-sporting use of the second largest Parisian sports facility.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".