Bibliographic record
Abstract
The aim of this study is to provide an estimate of the social costs of gambling in Italy. In line with other research on social costs, the present study estimates the consequences of gambling harm on public finances, focusing on the estimated costs to treat high-risk gamblers, costs associated with productivity losses, costs of unemployment, personal and family costs, crime and legal costs. We used two different approaches to calculate these costs. The first approach, used for health care costs, consists of using the lump sum spent to prevent the harm caused to high-risk gamblers. The second approach involves estimating the number of high-risk gamblers causing the cost, which is then multiplied with the average unit cost per person. Our estimates of the annual social costs of gambling in Italy – more than EUR 2.3 billion – demonstrate a substantial economic burden to society. However, the costs are a substantial underestimate, as they are limited to those of a public nature and do not take into consideration those costs borne by moderate and low-risk gamblers, as well as affected others.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".