Gambling Data and Modalities of Interaction for Responsible Online Gambling: A Qualitative Study
Bibliographic record
Abstract
Online gambling, as opposed to land-based gambling and other mediums of problematic and addictive behaviour such as alcohol and tobacco consumption, offers unprecedented opportunities for monitoring and understanding users’ behaviour in real-time. It also provides the ability to adapt persuasive messages and interactions that would fit the gamblers usage and personal context. These features open a new avenue for research on the monitoring and interactive utilization of gambling behavioural data. In this paper, we explore the range of data and modalities of interaction which can facilitate richer interactive persuasive interventions, and offer additional support to limit setting, with the ultimate aim of aiding gamblers, who gamble at low to moderate levels, to stay in control of their gambling experience. The exploration is based on our previous research on online addiction and interviews with experts (ne = 13) from different relevant multidisciplinary backgrounds and different points of view. We also interviewed gamblers (ng = 6) about their perception of the utilization of their data for aiding more conscious gambling. Directed at multiple stakeholders, including the gambling software providers, compliance and responsible gambling personnel, as well as policymakers, this paper aims to provide a basis and a reference point for empowering future responsible gambling socio-technical tools through the capture and utilization of relevant online gambling behavioural data.RésuméLe jeu en ligne, contrairement aux formes de jeu hors ligne et à d’autres types de comportements problématiques et de dépendance comme la consommation d’alcool et de tabac, offre des possibilités sans précédent de surveillance et de compréhension du comportement des utilisateurs en temps réel, ainsi que la capacité d’adapter des messages persuasifs et des interactions adaptées à l’utilisation des joueurs et au contexte personnel. Cela ouvre une nouvelle voie pour la recherche sur la surveillance et l’utilisation interactive des données comportementales relatives au jeu. Dans cet article, nous explorons à cette fin la gamme de données et les modalités d’interaction qui peuvent faciliter des interventions persuasives interactives plus riches et permettre un soutien accrû pour l’établissement de limites, dans le but ultime d’aider les joueurs de niveaux faibles à modérés à demeurer en contrôle de leur expérience de jeu. L’exploration est basée sur nos recherches antérieures sur la dépendance en ligne et sur des entretiens avec des experts (ne = 13) issus de différents contextes multidisciplinaires pertinents et ayant différents points de vue. Nous avons également interrogé des joueurs (ng = 6) à propos de leur perception de l’utilisation de leurs données pour contribuer à un jeu plus conscient. Ce document vise à fournir une base et un point de référence pour l’autonomisation de futurs outils socio-techniques du jeu responsable grâce à la saisie et l’utilisation de données pertinentes sur les comportements de jeu en ligne, et il est destiné à de multiples parties prenantes, notamment des fournisseurs de logiciels de jeu, du personnel de conformité et de jeu responsable ainsi que des décideurs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".