The effect of gambling restrictions on the level of crime in the Czech regions
Bibliographic record
Abstract
The effect of gambling restrictions on the level of crime in the Czech regions Abstract Between 2010 and 2020, the number of municipalities in Czechia that re- stricted or completely banned gambling activities within their territories significantly increased. A common argument for these restrictions was the concern that the availability of gambling might negatively affect crime rates. Among the most common types of crimes committed by gamblers are prop- erty crimes. This thesis examines whether the sudden and persistent elim- ination of land-based gambling facilities had a causal effect on the number of property crimes in small and medium-sized municipalities in Czechia. Us- ing quarterly panel data from 2016 to 2020, the study applies three econo- metric methods: Difference-in-Differences, Synthetic Control, and Synthetic Difference-in-Differences. The results consistently show no statistically sig- nificant evidence that gambling bans reduced property crimes. Further- more, this thesis highlights that the use of the recently developed Synthetic Difference-in-Differences estimator enables obtaining robust causal estimates even in settings where the key assumptions of traditional models are not con- vincingly satisfied. 1
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".