7. The British Migration State: Britain's Internal Controls on Immigration Through Welfare Policy
Bibliographic record
Abstract
By looking at the main trends and major changing points of internal immigration control, this project highlights the way in which the social security system has been transformedto become a tool of governmental policy enforcement rather than the repository of universal rights ideology. The shift in the value placed on and treatment of migrants overthe past 30 years has been substantive. Once a necessary and sought after supply of labour and demographic growth, newcomers have become a stigmatized ethnic category,a danger to societal cohesion and a parasite on the welfare system. In order to constantly manage and monitor them, the government has exploited the close relation betweenbenefits and immigration to create an unstructured, yet increasingly visible, internal control system. An analysis of policy will show how every act slowly increases theresources allocated for direct enforcement of immigration controls by bringing new employees into the network of those responsible for indirect enforcement such asimmigration officers, police, health or education staff, or even employers. By spreading the responsibility for enforcement among a variety of agencies (both state and private)whose concern is not immigration, the British Home Office created a machinery specifically designed to have power over every aspect of an immigrant or asylum seeker’sexperience in the United Kingdom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".