Bibliographic record
Abstract
Abstract Theories of precarity have emphasized workplace isolation, worker vulnerability and a lack of control over key features of work. Migration status has been viewed as an attribute that can exacerbate worker precarity, and sexual violence and bodily injury are viewed by feminist scholars including Violence Against Women scholars as sources of such precarity as well. Nevertheless, how the interaction of workplace conditions, migration status, gender and sexual violence impact migrants needs more attention. A new evidence base, the Migrant Worker Rights Database, explores workplace violations against migrants in 907 tribunal and court cases brought by migrants in Australia, Canada, the United Kingdom, and the United States over a 20‐year period. The data collected for this project demonstrates that female migrants experience higher rates of sexual harassment, sexual assault, sexual servitude, and sex trafficking when compared with men. Further, while such collectively termed “sexual violence” offenses comprise a small percentage of cases in the Database (1.3%), they are characterized qualitatively by key features that present a heightened form of sexual precarity when compared with citizens: misuse by employers of visa conditions, debt bondage, live‐in arrangements, entrapment and slavery, and the combination of sexual violence with economic infringements such as wage theft and physical assault. Sexual precarity, this paper argues, should be viewed as an overlapping and reinforcing form of workplace precarity that has distinctly sexual and bodily dimensions.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".