From problem gambling to crime? Findings from the Finnish National Police Information System
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
In previous studies, problem gambling was found to have many adverse consequences, including crime. However, links between crime and problem gambling have been studied relatively little. To fill this gap, we collected problem gambling-related police reports from the Finnish National Police Information System. Fifty-five problem gambling-related crime incidents reported to the police 2011 in Finland were subjected to qualitative analysis. The role of problem gambling, as self-identified by the gamblers themselves, was examined as highlighted in different crime reports: what common features did the gamblers share, and what were the possible causal mechanisms between problem gambling and crime? The data consisted of text documents produced by the police, specifically crime reports and preliminary investigation documents. Collected documents were coded using Weft QDA and SPSS. Grounded theory approach was applied. The majority of the cases were non-violent property crimes, committed at home or at the workplace. We determined that problem gambling, through financial difficulties, does indeed lead to crime.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it