What fresh hell? UK policies targeting homeless migrants for deportation after Brexit and Covid-19
Bibliographic record
Abstract
Before Covid-19 around a quarter of the UK’s rough sleeping population were non-UK nationals, with the proportion rising above half in some metropolitan areas. The UK government’s targeting of rough-sleeping EU citizens for ‘administrative removal’ between 2010–2017 reflected a trend in social policy towards enforcement- rather than support-based ‘solutions’ to migrant homelessness. Changes to the Immigration Rules in late 2020 to make rough sleeping a ground for refusal or cancellation of permission to be in the UK represent a revival of this tendency. This commentary analyses UK policies targeting rough sleeping non-UK nationals for deportation from a practitioner’s perspective – the author ran a rights project for homeless migrants between 2018 and 2021.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".