Bibliographic record
Abstract
The parallels between gambling and other addictive behaviours, such as alcohol abuse and alcohol dependence, suggest that theoretical and empirical work derived from the study of alcohol consumption may be useful in understanding aspects of gambling behaviour. One approach to understanding drinking behaviour in a population is the distribution of consumption model first proposed by S. Ledermann in 1956 and significantly extended by O.-J. Skog. This "distribution of consumption model" suggests that alcohol consumption in a population will be highly skewed to the right, i.e., toward higher consumption levels, be characterized by the lognormal distribution, and that the shape of this distribution is due to the multiplicative combination of contributing factors. Social interactions in the population are considered to be a primary contributing factor. The distribution of consumption model has been used to link increases in alcohol availability to increased average consumption, the increase in average consumption to increases in heavy consumption (as predicted by the distribution of consumption model), and increases in heavy consumption to increases in alcohol related problems. The present study has tested the applicability of the distribution of consumption model to the five year study of gambling in the City of Windsor, Ontario. Strong support has been found for the applicability of the distribution of consumption model to gambling consumption. The implications of the distribution of consumption model as it applies to gambling are discussed.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .G68. Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4785. Adviser: G. R. Frisch. Thesis (Ph.D.)--University of Windsor (Canada), 2000.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".