Examining the impact of mobile gambling harm minimisation features: a dualistic model of passion perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Driven by the ubiquity of smartphones, sports gambling has intensified globally. Most mobile gambling apps are mandated to offer harm minimisation features which are IT tools designed to help prevent harmful gambling activity. Existing research on the effectiveness of gambling harm minimisation features often overlooks the fact that individuals engage with multiple IT tools to varying extents to achieve a single goal. As an initial step, and to reflect actual user engagement, we conduct an exploratory factor analysis on a range of opt-in harm minimisation features. Next, aligned with the dualistic model of passion, we theorise and empirical test how direct and indirect harm minimisation features moderate the translation of different passions for mobile gambling into the well-being outcome of subjective vitality. Our findings suggest that indirect harm minimisation features, but not direct features, are effective in protecting the well-being of obsessively passionate mobile gamblers. For harmoniously passionate mobile gamblers, the opposite situation holds – direct harm minimisation features strengthen the effect of a harmonious passion on vitality whereas indirect features have no significant effect.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it