Gambling despite financial loss—the role of losses disguised as wins in multi‐line slots
Bibliographic record
Abstract
BACKGROUND AND AIMS: Slot machines pose serious problems for a subset of gamblers. On multi-line slots, many small credit returns are less than one's spin wager, resulting in a net loss to the player. These outcomes are called 'losses disguised as wins' (LDWs). We aimed to show that different proportions of LDWs could differentially affect gambling persistence (continuing to gamble despite financial loss), but that such LDW effects may depend on problem gambling symptomatology. DESIGN: Gamblers were randomized to play 100 spins on a game with few, moderate or many LDWs (between-subjects design), then continued playing for as long as they wished during (unbeknown to players) a losing streak (to measure gambling persistence). SETTING: A custom-built casino in a gambling research laboratory in Waterloo, Canada. PARTICIPANTS: Experienced gamblers (n = 132) with varying levels of problem gambling symptomatology from the Waterloo, Canada community. MEASUREMENTS: We measured the number of voluntary spins participants played (persistence) during the losing streak following the 100-spin playing sessions. We measured problem gambling symptomatology using the Problem Gambling Severity Index, and classified them as non-problem (n = 53), low-risk (n = 55) or higher-risk (n = 24) gamblers. FINDINGS: Persistence trends differed depending on LDW frequency and problem-gambling status (interaction: P = 0.037). High-risk gamblers showed a 'sweet spot' for LDW reinforcement, persisting for longer in the moderate than few or many LDW games (quadratic trend across LDW games: P = 0.028). Non-problem gamblers showed a linear trend across LDW games, gambling for longer in the few LDW game (P = 0.007). For low-risk gamblers, the quadratic contrast was not significant, P = 0.032. CONCLUSIONS: Multi-line slots contain outcomes in which one gains less than the original wager (losses disguised as wins or LDWs). Moderate (versus few and high) proportions of LDWs appear to make higher-risk players gamble for longer despite financial loss.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".