An Ecological Approach to Electronic Gambling Machines and Socioeconomic Deprivation in Germany
Bibliographic record
Abstract
In Germany, gambling research has primarily focused on the broader population in prevalence studies, neglecting the importance and influence of the local socioeconomic context in the development and maintenance of gambling disorders. To analyze the interplay between contextual and compositional factors in the market for electronic gambling machines (EGMs) in Germany, we assessed the EGM densities and socioeconomic deprivation in 244 local communities within Baden-Wuerttemberg. Our results suggest that EGM density is statistically associated with 3 socioeconomic determinants: The shares of migrants, unemployed, and high-school-educated people in the communities are statistically significant variables in our linear regression model, whereas younger age, male gender, and marital status exhibit no statistical associations with EGM density. The share of unemployed people is the only variable of statistical and practical significance. Our analysis advocates area-based policy measures to minimize gambling-related harm. By decreasing EGM densities in communities with high levels of unemployment, we expect to protect at-risk population strata that are most vulnerable to gambling exposure.
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.
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 | 0.001 | 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 | 0.000 |
| 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 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".