Immigrants, Natives and Crime: A Cross-sectional and Longitudinal Analysis.
Bibliographic record
Abstract
This study purpose is to verify if there is an association between foreign immigration and crime. In doing this, the study investigates also some satellite questions revolving around this possible association: the range of offences affected by immigration, the relationship between immigrant and native crime, and whether the immigration impact on crime is direct or indirect. These issues have been addressed through both a cross-sectional and a cross-sectional/time analysis. This double approach intends to find out whether variations over time in immigration and in crime confirm the synchronic analysis results, which could be biased by non-observed factors. The research is based on data of the Italian provinces. Italy represents a critical case for studying the migration-crime relationship, because in this country the rise in foreign immigration has been sudden and its pace feverish. The cross-sectional analysis findings show that crime rates are related to time-invariant factors and only marginally to immigration. On the contrary, the cross-sectional/time analysis shows that variations in immigration have had a positive impact on both the most serious and the most common offences. There is no evidence of indirect effects of immigration on crime or of a link with native crime. In contrast to previous literature regarding the U.S., Canada, and Australia, these results suggest that a tumultuous rise in immigration can affect crime rates.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".