Setting a hard (versus soft) monetary limit decreases expenditure: an assessment using player account data
Bibliographic record
Abstract
Considerable debate has focused on whether pre-commitment to a money and/or time limit and adherence to that limit should be mandatory or voluntary. A unique feature of Ontario Lottery and Gaming’s player management system (My PlaySmart) provides a middle ground by allowing players to select whether they are permitted to continue playing once their limit is reached (the soft lock option) or whether continued play is not permitted (the hard lock option). We assessed the relative responsible gambling utility of these two options using player account data by comparing play data before and after enrollment. Players who chose the hard lock option decreased their average coin-in, loss per visit, and minutes played per visit. In contrast, soft lock enrollees significantly reduced their coin in (but not to the same extent as those who chose the hard lock option) and increased their visits from pre-enrollment to post-enrollment. The play data for hard and soft lock enrollees was also benchmarked against play of non-enrollees. Results suggest that the soft lock option is relatively ineffective at limiting play, thus adding important knowledge to the ongoing debate about pre-commitment schemes that aim to advance responsible gambling to minimize gambling-related harms.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".