Bibliographic record
Abstract
A great tool to grow an empire, detention camps have been historically used and abused as a subordination tactic by all of the most powerful empires, most notably by Germany’s Nazi Regime in the Second World War. Commonly thought to have been born and died in history, they are a mostly forgotten piece of the past. Many would be shocked to discover that there is a secret world of institutions that disregard international human rights law in order to prioritize their national goals, where refugees, asylum seekers and migrants are the primary victims. Systems for migrancy and immigration detention have emerged in the wake of globalization, seeking asylum from conflicts, natural disasters or financial insecurity, or simply searching for better economic opportunities, education or reunion with family. Multicultural liberal democracies use detention centres to enforce their racialized and gendered penal power and establish a national hierarchy where poor, young men of colour are the most marginalized. This paper critically examines the victimization of male refugees, asylum seekers and migrants by neo-colonial masculinities in Canadian, American and British immigration detention centres and how these experiences create offenders through the victim-offender overlap.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".