La méthodologie au service de l'avancement des connaissances en matière de jeux de hasard et d'argent
Bibliographic record
Abstract
Cet article passe en revue trois méthodologies susceptibles de faire avancer les connaissances en matière de jeux de hasard et d'argent: la méthodologie longitudinale, la méthodologie expérimentale et la méthodologie génétique. Des exemples servent à illustrer comment ces méthodologies permettent: a) de documenter le développement des habitudes et des problèmes de jeu; b) d'identifier les facteurs de risque à présomption causale associés à leur apparition, à leur maintien ou à leur aggravation; c) d'explorer la signification de la cooccurrence des problèmes de jeu avec d'autres problèmes de santé mentale; d) d'analyser les possibles liens transactionnels entre les problèmes de jeu et d'autres problèmes d'adaptation; et, enfin, e) de dégager des leçons par rapport à la modélisation théorique et à l'intervention préventive.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →2 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Reviews longitudinal, experimental, and genetic study designs and what they enable for advancing gambling knowledge; the object is research methodology and study design, though it is field-oriented.
This explicitly examines research methodologies used to advance knowledge in gambling studies.
Reviews longitudinal, experimental, and genetic methods as tools to advance gambling knowledge; domain methods guidance, not study of research itself.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".