Les jeux vidéo pour enfants: Que savons-nous
Bibliographic record
Abstract
Combien de fois a-t-on entendu denoncer les effets pervers des jeux video sur les comportements et les bonnes habitudes de vie des enfants et des adolescents ? Quelle difference faire entre le langage populiste et la realite ? Les professionnels du loisir sont souvent appeles a repondre a des questions ou a mettre en perspective des opinions sur des pratiques emergentes en loisir. Voila pourquoi l’Observatoire quebecois du loisir presente ce bulletin : pour repondre precisement a cette question, sachant qu’a ce jour le chiffre d’affaires des jeux video depasse celui du cinema et que les jeux video occupent de plus en plus une bonne partie du temps libre non seulement des enfants, mais de toute la population. Au dernier Congres mondial du loisir tenu en Coree du sud, en conference principale, la professeure Olson, de l’Universite Harvard, a presente les resultats d’une recherche sur l’omnipresence de la violence dans les jeux video et l’influence de la pratique de ces jeux sur la creation de pensees, les emotions et les comportements violents chez les enfants et adolescents. Les resultats de cette recherche demystifient les effets nefastes des jeux video, mais les chercheurs font des recommandations aux parents qui ont fait grand bruit aux Etats-Unis. Le present bulletin presente les resultats d’une recension etendue des ecrits, ainsi que de sondages et groupes de discussion.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".