Decarceral Futures: Bridging Immigration and Prison Justice towards an Abolitionist Future
Bibliographic record
Abstract
This special issue focuses on what a standpoint of carceral abolitionism brings to citizenship studies, with immigration detention as the key case study. The nine articles and editorial introduction probe the intersections of detention with current and potential forms of citizenship. The contributions collectively emphasize what citizenship studies also documents: similar to how the prison is a site of social control, immigration control is a nation-building site where access to permanent status and citizenship is closely filtered along racial, gender, class, ableist, and other lines of discrimination. Employing a plurality of case studies spanning North America, Europe, and Asia, and coming to the subject from a spectrum of interdisciplinary backgrounds, all contributors nonetheless foreground the recognition that deprivation of liberty is one of the most serious harms that someone can experience. Like the activists protesting police brutality around the world, the special issue contributors are thinking across the spectrum of de-funding policing, overhauling the ‘criminal justice’ system, eradicating prisons (penal abolitionism), and doing away with all forms of containment (carceral abolitionism). The collective findings reaffirm that neither the prison nor the detention centre are inevitable in the modern, democratic order. Abolishing all forms of immigration detention would open the door for the emergence of new visions of justice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".