Motivations to Regulate Online Gambling and Violent Game Sites
Bibliographic record
Abstract
With online gaming becoming a major entertainment form, there are growing concerns that websites promoting gambling and violent games have undesirable effects. Such concerns have led to numerous calls to regulate controversial gaming sites. However, little research has been done to explain why people support restrictions on gaming sites. One theory, the third-person effect, provides a possible explanation. The third-person effect suggests that when confronted with a negatively perceived message, people tend to overestimate the message’s effect on others compared to one’s self. This perceptual disparity motivates people to take action against such messages. In a survey of 184 adults, this study found that people perceive gambling and violent game sites to have a greater effect on others than on themselves, and the third-person perception significantly contributes to predicting censorship attitudes. This study also found that age and gender play a part in explaining the magnitude of the third-person effect and the link between third-person perception and censorship attitudes. Public policy implications relating to regulation of gaming sites are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".