Bibliographic record
Abstract
RÉSUMÉ:Nul ne peut ignorer que les centres d’entrainement physique sont plus que jamais fréquentés. Particulièrement populaire, l’entrainement prend désormais une grande place dans la vie de plusieurs. Habitude saine, certes, mais jusqu’à une certaine limite. Cette limite se trace par ailleurs au moment où l’un des résultats de l’entrainement musculaire, soit une apparence corporelle musclée, deviennent l’unique préoccupation. La dysmorphie musculaire, ou communément appelée la « bigorexie », est le nom que l’on donne à ce trouble psychologique. Il décrit l’obsession compulsive de l’entrainement et des muscles. Cette maladie étant complexe, de raisons variées et d’importantes conséquences et complications s’y rattachent.ABSTRACT:No one can ignore that physical training centers are more popular than ever. With its increased popularity, training now takes an important place in the lives of many. Although certainly a healthy habit, there are limits. These limits become evident when one of the results of strength training, a muscular body appearance, becomes one’s only concern. Muscle dysmorphia, or commonly called “bigorexia,” is the name we give to this psychological disorder. Bigorexia is a compulsive obsession with training and a muscular build. This disease is complex, for varied reasons and related to important consequences and complications.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".