Rearrests of Noncitizens Subsequent to Immigration Removal From the United States
Bibliographic record
Abstract
Deportation or removal from the United States for criminal justice–involved noncitizens has been described as analogous to incapacitation. A common assertion is that if immigration authorities remove these noncitizens from the United States, future criminal justice involvement will be averted. The present study explores the hypothesized incapacitation effect of immigration removal and tests whether a record of prior removal predicts postremoval rearrest patterns. The sample consists of 521 foreign-born males with a verified immigration removal from the United States, following transfer into federal immigration custody from Los Angeles County Jail in 2002. California rearrests after the date of verified U.S. removal were tracked through 2011. Results indicate that 48% of the sample was rearrested at least once and 22% had three or more postremoval arrests. These findings do not support the hypothesis that deportation equates to permanent incapacitation. The study also found that a record of prior removal did not predict postremoval rearrest likelihood or frequency. As a single longitudinal study and the first of its kind, these results alone cannot inform responsible policy recommendations. The study does, however, highlight directions for further research and the pressing need for access to individual-level immigration data for empirical study and public distribution of results.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".