De la rue et des skateparks aux musées : de l’influence d’une recherche ethnographique
Bibliographic record
Abstract
The author draws on professional experience from the late 1980s to the mid-2000s. He held a special institutional position that combines life as a sociologist in the field with participants in varied lifestyle in museums: collector of objects and archives, scenographer, designer, exhibition installer and creator of collections documented by surveys. He proposes the analysis of the entry of skateboarding into a French national museum (the MNATP in Paris, which became Le MuCEM in Marseille), as well as to question, on the one hand, a manufacturing company in the history of skateboarding seen from a French perspective in its complexity, and, on the other hand, an activity of creation and exhibition installation that is finalized by an action of both material and immaterial heritage enhancement. What to select and conserve? What is a beautiful object for a researcher, for a museum curator? The article tries to shed light on the complex processes of recognition, legitimization (in which the author participates), of a leisure activity on the border of play and sport, of the educated and the popular, of seriousness and futility, of a practice that has often claimed to be alongside or against institutions such as sports: a human and intellectual adventure that has tried to combine scientific content and value for the general public and practitioners.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".