Challenging Immigration Detention: Academics, Activists and Policy-Makers. By Michael J. Flynn and Matthew B. Flynn (eds)
Bibliographic record
Abstract
The proliferation of immigration detention around the world has been accompanied by growing interest in, and critical attention to, this policy and practice amongst academics, activists, policymakers, journalists and members of the general public. Challenging Immigration Detention—a collection edited by Michael J. Flynn and Matthew B. Flynn—is a helpful addition to the international discussion and debate about migrant detention (see e.g. recent edited collections by Nethery and Silverman 2015; Furman et al. 2016; Conlon and Hiemstra 2017). What sets this volume apart is its focus on the complicated question of how to challenge the policy and practice of detention while also working to make it less harmful for those who are detained. Comprising 14 chapters along with an introduction and conclusion, the collection covers a range of issues, perspectives and jurisdictions, with contributors who are (as the book’s subtitle indicates) academics, activists and policymakers. Topics include deaths in detention and family detention in the United States; inspecting detention in the United Kingdom; youth-activist infiltrations of detention centres in the United States; international advocacy and alternatives to detention; legal frameworks in South America, the European Union and other countries of the Global North; and the provision of mental health care in Australian detention facilities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".