Bibliographic record
Abstract
Des affaires judiciaires de matchs truques lies aux paris touchent regulierement la ligue taiwanaise de baseball professionnel depuis le milieu des annees 1990. Elles impliquent chaque fois un grand nombre de joueurs et d’entraineurs, des personnalites politiques locales et des membres de la police. L’acte de tricherie constitue une rupture du contrat moral qui lie les joueurs professionnels a leurs supporters. Ces joueurs sont le plus souvent designes par l’animal emblematique de leur club auquel est attachee l’epithete « noir » qui souligne la perception negative de ces agissements et les relient implicitement aux organisations criminelles qui controlent le milieu des paris. Ces pratiques de corruption sont imbriquees aux reseaux etendus d’interconnaissance dans lesquels les joueurs s’inscrivent. Ces derniers se trouvent ainsi pris entre plusieurs espaces d’obligations sociales et sous la menace d’une radiation a vie de la ligue. Une analyse des matchs truques relatifs aux paris permet dʼapprehender comment les valeurs morales structurent la pratique du baseball a Taiwan et la facon dont elles sont negociees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".