Applying Self-Coding to the Measurement of Self-Generated Video Gaming and Gambling Memory Associations
Bibliographic record
Abstract
Recently the use of implicit memory associations has expanded in the addiction literature to include the assessment of video gaming and gambling. However, the issue with memory associations lies in the open-ended nature of the answers that must be coded, which is often labour-intensive, costly, and where the ambiguity cannot always be resolved. The present study evaluates participant self-coding of memory associations versus researcher coding in the assessment of memory associations for video gaming and gambling. A sample of 3,176 Canadian adults were asked to produce responses to ten ambiguous words and ten potential behavioural associations for engagement in video gaming or gambling. Participants were subsequently asked to classify what categories their responses belonged to, including video gaming and gambling. Consistent with the literature on alcohol and marijuana memory associations, self-coded scores for video gaming and gambling were significantly higher than scores coded by the researchers, had significantly higher correlations with self-reported behaviours, and significantly improved the prediction of video gaming and gambling behaviours.Résumé L’utilisation des associations de la mémoire implicite comprend depuis peu l’évaluation des jeux vidéo et des jeux de hasard dans le vocabulaire de la dépendance. Le problème des associations de la mémoire réside toutefois dans le caractère ouvert des réponses qui doivent être codées, ce qui est souvent à haute intensité de main-d’œuvre et coûteux. De plus, il est parfois impossible de résoudre l’ambiguïté. La présente étude évalue l’autocodage par les participants des associations de la mémoire par rapport au codage effectué par le chercheur dans le cadre des jeux vidéo et des jeux de hasard. On a demandé à un échantillon de 3 176 Canadiens d’âge adulte de répondre à dix mots ambigus et dix associations comportementales potentielles en rapport avec la participation à des jeux vidéo ou des jeux de hasard. On leur a ensuite demandé de classer leurs réponses dans différentes catégories, y compris les jeux vidéo et les jeux de hasard. Tout comme dans la documentation sur les associations de mémoire dans le domaine de l’alcool et de la marijuana, les pointages autocodés pour les jeux vidéo et les jeux de hasard étaient considérablement plus élevés que ceux codés par les chercheurs, faisaient l’objet d’une corrélation beaucoup plus élevée avec des comportements autodéclarés, et amélioraient considérablement la prédiction de comportements liés aux jeux vidéo et aux jeux de hasard.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".