Immigration Detention: The Migration of a Policy and Its Human Impact
Bibliographic record
Abstract
Immigration Detention is a collection of 16 case studies focusing on the diffusion and expansion of immigration detention policies, and the prospects of protection for people seeking asylum. As well as describing various detention policies, it provides an insightful analysis of the widespread practice of deterring illegal/irregular migrants and asylum seekers from crossing State borders to seek international protection. This policy approach is motivated by governments framing the immigration and asylum debate as a security threat. As a result, illegal/irregular immigrants and asylum seekers are predominantly regarded as persons who pose a threat to the security or identity of a particular State. Individual chapters, each assessing a specific country, follow a clear and more or less similar structure, which allows some comparisons. The United Kingdom, the Netherlands, France, Finland, Malta and Cyprus, Turkey, the United States, Canada, and Australia are covered. In addition, the book includes case studies that are not as commonly considered in academic research, such as Cuba, Papua New Guinea, Indonesia, Malaysia, Mexico, South Africa, and Israel.
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 | 0.002 | 0.005 |
| 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 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".