The legalization process of mixed martial arts and its effects on both practice and practitioners: The case of France
Bibliographic record
Abstract
As a genuinely contemporary sport, mixed martial arts (MMA) was born and developed throughout the twentieth century, during which time it remained fairly marginal, only to find a more formal and mediatized format in the 1990s. The first legal events in France began taking place in 2020. Consequently, how did legalization influence the evolution of MMA practice, both in training rooms and during competitions? The research is primarily based on 21 semi-directive interviews and the observation of five events (pankration/kempo rules) in France, between 2011 and 2015. The last step included the legalization phase of the sport between 2019 and 2022 and four professional events observed in France (MMA rules). Ten interviews were carried out with federal or organizational figures, in addition to trainers and fighters. The main result shows that MMA in France was already sportivized before legalization. Legalization tends to reassure individuals and parents.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".