Bibliographic record
Abstract
In this co-authored editorial introduction, Evelyne Massa and I provide a context for our special issue by outlining the major approaches and issues related to immigration detention in liberal, democratic states. We are concerned that there is no commonly accepted definition of detention, and so we endeavor to provide one here. Beyond the need for greater conceptual clarity, we suggest that a pressing question is why immigration detention is continuously expanding despite mounting evidence that the practice harms people, does not deter irregular immigrants, and fails to ensure more efficient and effective immigration and asylum determination systems. We introduce the major themes from each contribution and explain how they both address this question and initiate new ones. Collectively, these papers demonstrate that immigration detention is embedded in, and essential for, wider immigration and penal apparatuses; yet, it also operates by its own logics, which in turn shape population boundaries in new ways both within and beyond the sovereign state. We conclude that immigration detention warrants special critical attention and a more robust research agenda.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".