Dismantling Detention: International Alternatives to Detaining Immigrants
Bibliographic record
Abstract
As the harmful effects of immigration detention become more widely known and the appropriateness of detaining migrants is increasingly questioned, governments are looking at alternatives to detention as more humane and rights-respecting approaches to addressing the management of migrants and asylum seekers with unsettled legal status. This report examines alternatives to immigration detention in six countries: Bulgaria, Canada, Republic of Cyprus, Spain, the United Kingdom, and the United States to highlight viable, successful alternatives that countries should implement before resorting to detention. While the report provides an analysis of specific alternatives to detention (often referred to as ATDs) in each country, it is not intended to provide a comprehensive overview of all alternative programs available.Each country featured in this report has taken a different approach to alternatives to detention. Some focus more heavily on surveillance and others on a more person-centered, holistic approach. Ultimately, this report finds alternatives that place the basic needs and dignity of migrants at the forefront of policy, such as community-based case management programs, offer a rights-respecting alternative to detention while simultaneously furthering governments' legitimate immigration enforcement aims.
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 | 0.004 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".