Latino Farmworkers in Saskatchewan: Language Barriers and Health and Safety
Bibliographic record
Abstract
As part of a study focused on the experiences of Latino migrant farmworkers in Saskatchewan, Canada, we have attempted to understand how language barriers (LBs) broadly understood may affect farmworkers and their employers, workplace communications, and occupational health and safety (OHS). Drawing on critical ethnography and intercultural communication theory, qualitative interviews were conducted with 39 Latino migrant farmworkers, 11 farmer-employers, two OHS civil servants, and two former Canadian farmworkers. Our findings suggest that LBs interfere with the establishment of effective communications between Latino farmworkers, other farm enterprise personnel, civil servants, and health services providers. LBs impede establishment of the kinds of sustained two-way communications needed for maintaining safe and healthy working environments. All of the stakeholders involved were found to contribute in some manner to the propagation of LBs. The risks for the physical and psychological well-being of migrant farmworkers are substantial, but despite the fact that LBs are generally recognized as a challenge and as a source of risk, they are not widely seen as warranting any systematic response. It is critical that Latino migrant workers learn more English and that their Canadian employers and supervisors learn more Spanish. Beyond that, there is an urgent need for a multistakeholder coalition that moves to address LBs by training certified interpreters and liaison personnel who can facilitate better communications between migrant workers, their employers, and other stakeholders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".