The total consumption model applied to gambling: an analysis of gambling accounts records in Norway
Bibliographic record
Abstract
Background The total consumption model (TCM) posits a positive association between total consumption and rate of excessive consumption or related problems in a population. In this study we examined whether TCM applies to gambling.Method We employed tracking data from 40 000 customers at a Norwegian gambling monopolist, Norsk Tipping (NT). For 14 population groups, we examined distribution characteristics of total net losses on gambling in a calendar year; total consumption (mean) and dispersion (percentile values) and rates of excessive gambling (i.e. exceeding the 95th or 98th percentile in the total sample). Associations between total consumption on the one hand and rates of excessive gambling and percentile values on the other were estimated in linear regression models.Results We found positive and statistically significant associations between mean gambling consumption and rates of excessive gambling. We also observed positive and statistically significant associations between population mean and percentile values (25th, 50th, 75th, 90th and 95th) and thus a clear pattern of regularity in the distribution of gambling losses across populations with different total gambling consumption.Conclusion The findings lend support to the validity of the total consumption model with regard to gambling.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".