Exporting and importing criminality: Incarceration of the Foreign Born
Bibliographic record
Abstract
Since previous studies have found that crime rates vary by immigrant group there is a need to dis‐aggregate immigrants by country of birth in order to obtain a more accurate representation of the relationship between migrants and crime. This study examines data from six countries (Australia, Canada, France, Italy, the Netherlands, and the U.S.A.) on the country of birth of their inmate populations. The following observations are reasonable conclusions from the data available. First, the percentages of each home country's inmate population that is foreign‐born varies remarkably. Second, in general foreign‐born inmates tend to come from regions outside the region within which the host country was located, though in most cases from regions that were proximate. Third, given the small number of countries reporting, it is intriguing that just a small number of countries and regions can account for such a high proportion of a home country's inmate population if one includes the numbers of a country's citizens who are housed in foreign prisons as part of that original country's inmate population. The paper concludes with a discussion of a number of policy implications that flow from these findings.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".