Experiencing a significant win and its socio-demographic and motivational predictors: A comparative analysis between pure-chance gamblers from Poland and France
Bibliographic record
Abstract
The research on the relationship between wins and gambling behaviour most often focuses on winning considerably large amounts of money. It seems, however, that it is not the amount of the win that exerts a decisive influence on gambling behaviour but the significance that the player assigns to the win. Therefore, we adopted the concept of “significant win”, a win perceived by gamblers as important to them. The research aimed to discover what kind of wins are experienced as significant and what factors explain experiencing wins as significant. The research conducted in Poland (N = 3,143) and France (N = 5,692) also had a comparative goal: discovering intercultural differences in experiencing significant wins. The computer-assisted web survey was conducted among gamblers engaging in pure-chance gambling, selected from representative samples in both countries. Logistic regression models were used to examine predictors of significant win experience in both countries and cross-countries differences between them. The results demonstrated that Polish gamblers more frequently considered wins significant when accompanied by strong, often negative emotions and were objectively higher than French gamblers. A significant win was more frequently associated with a positive experience in the view of French gamblers. The common predictors of a significant win experience in both countries were: being in debt, experiencing the win of a close person, gambling in a game of pure chance other than lotteries, more systematic pursuit of gambling, enhancement and coping gambling motivations. The age of the initiation into gambling was a significant predictor only in the French sample, while financial motivation – in the Polish one. The results confirmed that the subjective perception of gambling wins is only partially related to the amounts of wins, which has practical implications for planning prophylactic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".