The geography of gambling: A socio-spatial analysis of gambling machine location and area-level socio-economic status
Bibliographic record
Abstract
This study mapped the geographical location and density of electronic gambling machines (EGMs) in Denmark and investigated whether gambling machines cluster in areas with specific socio-economic status (SES) characteristics. Using micro-area modeling and inverse probability weighted regression adjustments, the study was based on register data on SES, EGM location data and geographical grid data. Findings showed that EGMs were distributed throughout the country with some notable clusters in the larger cities. While identifying city-based hotspots, findings also indicated that pure population density offered merely partial explanations in term of EGM location. In terms of links between area-level SES and EGM density, the study found a significant and positive correlation between low level of SES and EGM density. This study could inform fine grained geographical risk localization and harm minimizing measures that transcends well-known administrative area classifications.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".