Bibliographic record
Abstract
Abstract Security is more than ever a central theme in the study of international migration. For the past twenty years, research on the securitization of migration has burgeoned. While these initiatives are to be applauded, we believe they may also have misdiagnosed the problem. For example, it may not be that the concept of ‘security’ needs to be ‘humanized’ in order to be more in tune with migrants’ concerns. Rather, the problem may lie in the use of the ‘migrant’ as an analytical category. The ‘migrant’ remains an inherently statist construct. The starting premise for the collection of articles in this special issue is that it is the tendency of academic research to mistake the statist category of the ‘migrant’ as an analytical category that has prevented the literature on the migration–security nexus from meaningfully reflecting the lived experience and aspirations of its human respondents, particularly as regards their encounters with forms of institutional authority, practices, resistance and resilience. We use the rubric of deportability to open up a variety of ways of thinking and talking about migration and security that do not fall back upon statist tropes. The authors in this collection take up this challenge by framing and employing concepts such as statelessness, sedentariness and expulsion to redefine our understanding of the relationship between movement and order. They take inspiration from multiple brands of social and political theorizing where conditions of violence and forced removal qualitatively differentiate the experiences and encounters of a particular group.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".