Bibliographic record
Abstract
Over the last 30 years, the detention of irregular immigrants, undocumented workers, and the incarceration of immigration “offenders” has been on the rise. After 9/11, the social construction of immigrants as a source of “danger” reached a new scale, and the mechanisms through which immigration enforcement reproduces the criminal justice system became even more sophisticated. This article discusses the expansion of migrant detention in the overall “punitive turn” of immigration enforcement, the frequent tension between the state’s practices of power and detainees’ human rights, and the ways through which the securitization of migration replicates the commonsense link among immigration, crime, and national (in)security. This article starts with the complex distinction between migrant detention and incarceration as two different but related state enforcement apparatuses. While detention is a form of “administrative confinement” characterized by a deprivation of liberty for immigration-related civil offenses, incarceration refers to a deprivation of liberty for violations of criminal statutes. Based on detainees’ experiences, this article further examines whether migrant detention should (or not) be conceptualized as punishment, its importation of carceral tactics, and the increasing punitiveness of detention in the 21st century. Global capitalism has made the expansion of migrant detention possible. The privatization of detention has not only brought an extension of institutions, means, agents, and enforcement facilities, but it has also produced enormous profits for corporations, transforming detention into a profitable private business. At the micro level, the privatization also creates a complex geography of detention, a set of politics of carceral time and space, and a tension between mobility and immobility. Overall, the article reviews the most prominent trends in migrant detention in the northwestern hemisphere and in jurisdictions usually neglected by the mainstream literature. The article pays attention to global trends in detention across the United States, United Kingdom, Canada, and northwestern Europe and also reviews understudied regions such as Australia, Indonesia, Greece, Sweden, Malta, and Norway, among others. The explicit effort to expand the mainstream literature’s Euro-American bias is here developed to problematize and expand the naturalized boundaries of immigration enforcement scholarship. At the same time, this bibliography emphasizes the need for developing more intersectional approaches on migrant detention particularly attentive to factors such as race/ethnicity, class, and gender. The authors would like to thank Mary Bosworth, Cesar Hernandez, David Hernandez, Nicholas de Genova, Amanda McDonald, and the reviewer for their insightful feedback on earlier drafts of this article. Any mistakes and shortcomings are our own.
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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 | 0.001 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".