Undesirable and Unreturnable Migrants : Policy challenges around excluded asylum seekers and other migrants suspected of serious criminality who cannot be removed. Conference report and policy brief
Bibliographic record
Abstract
Migrants who are undesirable because of alleged involvement in serious criminality but unreturnable because of legal or practical reasons may present decision makers, policy makers and the responsible politicians with significant challenges. While there are different short-term policy responses to the issue, a considerable group of these individuals will always remain in legal limbo, sometimes for many years. A coherent solution is currently lacking, as is guidance on how to deal with this group of persons. Building upon two network meetings with academics and practitioners, this document defines the problem, describes current state responses and explores possibilities of future policy solutions.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".