Mechanisms of migrant exclusion: Temporary labour, precarious noncitizenship, and technologies of detention
Bibliographic record
Abstract
Abstract ‘Mechanisms of Migrant Exclusion’ focuses on the exclusionary measures that migrant workers confront. Although migration studies have long attended to various social and structural systems of exclusion, for instance, xenophobia and nativism (De Genova, 2005, https://doi.org/10.1515/9780822387091 ; Golash‐Boza, 2011, https://doi.org/10.4324/9780203123928 , and 2015, https://doi.org/10.18574/nyu/9781479894666.001.0001 ), recent global shifts in immigration politics and temporary labour regimes have increased the urgency of attending to the rise of global and transnational systems or regimes of exclusion. Internationally, noncitizens have grown increasingly vulnerable to detention and deportation (Mountz, 2020, 10.5749/j.ctv15d8153), whereas migrant contract workers continue to be systematically denied rights and protections in the labour market (Strauss and McGrath, 2017, 10.1016/j.geoforum.2016.01.008). These mechanisms of exclusion illustrate the range of limitations faced by migrants, particularly those who are undocumented, refugees, or temporary workers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".