Is Previous Removal From the United States a Marker for High Recidivism Risk? Results From a 9-Year Follow-Up Study of Criminally Involved Unauthorized Immigrants
Why this work is in the frame
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Bibliographic record
Abstract
The present study examines the long-term recidivism patterns of a group of unauthorized immigrants identified to be at high risk of recidivism. Using a sample of 517 male unauthorized immigrants, we used three measures of recidivism to assess 9-year rearrest differences between unauthorized immigrants who have and who have not been previously removed from the United States. Results indicate that prior removal was a significant risk marker for recidivism, with previously removed immigrants showing a higher likelihood of rearrest, a greater frequency of rearrest, and a more rapid time-to-first rearrest. While the present study does not establish whether previous removal is a consistent indicator of high recidivism, it suggests that this group of unauthorized immigrants may be worthy of review and policy consideration. Much potential value for law enforcement lies in the sharing of federal immigration records with academics to further study the outcomes of unauthorized immigrants.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it