From Camps to Plants: Protection Meets Productivity for Resettled Refugees
Bibliographic record
Abstract
Based on original research with former refugees working in Canada’s meatpacking sector and their family members, this paper traces the work trajectories of refugees resettled to Canada as permanent residents (PRs) and examines how this work and the processes of resettlement can constitute a form of liberal violence. Resettled refugees to Canada are those who are selected overseas based on humanitarian criteria and come to Canada as immigrants with permanent residence. Yet, tension quickly emerges upon arrival between their protection and their productivity. By definition, refugees are at once fleeing violence, threats of human rights atrocities, or persecution, yet some arrive to face a liberal violence, or “slow harm,” which may not violate laws at first glance, but nonetheless does damage once in Canada. Governments send resettled refugees to cities of all sizes, including small population centers—where employment prospects are limited—to difficult and dangerous work in meatpacking. Some choose hazardous food-processing jobs because they cannot find steady employment elsewhere. The study reveals the contradictions that resettled refugees experience in Canada: while offered legal protection through resettlement, many are only able to find employment in survival jobs that have high rates of injury and relatively poor pay. The article first interrogates the term “protection” in relation to refugees resettled by and to a liberal democratic state, namely Canada. Attention is then drawn to the gap between humanitarian protection, as legal status, and socioeconomic stability, given the reality of living and working “close to the bone” in a small one-industry town. Keywords: refugees, liberal violence, protection, precarious work, Canada
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".