Policy Legacies, Visa Reform and the Resilience of Immigration Politics
Bibliographic record
Abstract
Comparative scholarship tacitly assumes immigration politics to be relatively rigid. A state's immigration policy legacy is said to institutionalise policy preferences, thereby making it difficult to implement lasting reforms that are inconsistent with that legacy. This presents difficulties for states with restrictionist legacies wanting to implement liberal reforms in response to the emergence of labour shortages or demographic problems. The supposed rigidity of immigration politics is scrutinised in this article through a systematic process analysis of developments in the United Kingdom over the past decade, where the Blair government confounded the UK's characterisation as a ‘reluctant immigration state’ to implement various liberal work visa reforms. The uncoordinated nature of policymaking and implementation, and the limited involvement of state and societal institutions in the reform process, reflect the UK's historical experience with restrictionist policies, and help to explain the subsequent reintroduction of strict visa controls. The case demonstrates that policy legacies indeed play a significant role in defining the character of the policymaking institutions that shape a state's immigration politics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".