Broken English, Broken Bones? Mechanisms Linking Language Proficiency and Occupational Health in a Montreal Garment Factory
Bibliographic record
Abstract
Language barriers are often cited as a factor contributing to ethnic inequalities in occupational health; however, little information is available about the mechanisms at play. The authors describe the multiple ways in which language influences occupational health in a large garment factory employing many immigrants in Montreal. Between 2004 and 2006, individual, semi-structured interviews were conducted with 15 women and 10 men from 14 countries of birth. Interviews were conducted in French and English, Canada's official languages, as well as in non-official languages with the help of colleague-interpreters. Observation within the workplace was also carried out at various times during the project. The authors describe how proficiency in the official languages influences occupational health by affecting workers' ability to understand and communicate information, and supporting relationships that can affect work-related health. They also describe workers' strategies to address communication barriers and discuss the implications of these strategies from an occupational health standpoint. Along with the longer-term objectives of integrating immigrants into the linguistic majority and addressing structural conditions that can affect health, policies and practices need to be put in place to protect the health and well-being of those who face language barriers in the short term.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".