Theoretical and Pragmatical Challenges in Game Studies in Brazil
Bibliographic record
Abstract
ABSTRACT Background: The video game industry is increasing worldwide and Brazil is the leading market in South America. Abuse of video games has been an international concern for a few decades and the World Health Organization included gaming disorder in the 11th version of the International Classification of Diseases. Method: In this article, we cite some academic works and our own experiences to reflect about obstacles commonly found by researchers, beginning with the lack of a consensual definition of what is gaming. Then, we show some characteristics of the Brazilian scenario regarding research about gaming in general and clinical practices about gaming disorder in particular and discuss how sociopolitical traits may impact them. Conclusions: Finally, we discuss some options to avoid pathologizing normal behaviour and the importance of connections between psychiatrists and other groups from within and outside academia, such as researchers from game design and politicians, respectively. Arrière-plan: L’industrie des jeux vidéo se développe dans le monde entier et le Brésil est le marché principal d’Amérique du Sud. L’abus de jeux vidéo est une préoccupation internationale depuis quelques décennies et l’Organisation mondiale de la santé a inclus le trouble du jeu dans la 11e version de la classification internationale des maladies. Mèthode: Dans cet article, nous passons en revue certains obstacles fréquemment rencontrés par les chercheurs, à commencer par l’absence de définition consensuelle de ce qu’est le jeu. Ensuite, nous montrons certaines caractéristiques du scénario brésilien concernant la recherche sur le jeu en général et les pratiques cliniques sur le trouble du jeu en particulier, et nous discutons de la manière dont les caractéristiques sociopolitiques peuvent les influencer. Conclusion: Enfin, nous discutons de certaines options pour éviter de pathologiser un comportement normal et de l’importance des liens entre les psychiatres et d’autres groupes à l’intérieur et à l’extérieur du monde universitaire, tels que respectivement les chercheurs en conception de jeux et les politiciens.
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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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".