Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it