Social Representations of Responsibility in Gambling among Young Adult Gamblers: Control Yourself, Know the Rules, do not become Addicted, and Enjoy the Game...
Bibliographic record
Abstract
The responsible gambling approach is the subject of significant debate in the scientific community due to its tendency to individualize responsibility, focusing heavily on the gambler’s responsibility for gambling-related harm. Despite the gambler, and their responsibility, being the focus of responsible gambling discourse, their voices and perspectives remain largely absent. This study aims to address this limitation by documenting the social representations of the concept of responsibility held by gamblers themselves. How does the gambler perceive the concept of responsibility? Do they have an individual-centred understanding of this concept or are they able to distinguish their individual responsibility from that of the other stakeholders? This qualitative research is based on semi-structured interviews with 30 young adults (aged between 18 and 30 years old) who participated in gambling activities in the year preceding the research interview (2018). The results reveal that the social representations of responsibility held by gamblers fit into five categories: self control, knowing the rules and making the right decision, enjoying the game, not becoming an addict, and preventing harms related to gambling. All of these categories were found to be rooted in an individual perspective of responsibility. These results are discussed in light of the process of constructing the social representations of responsibility within the responsible gambling approach and in a neoliberal context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".