Occupational Health and Safety Challenges From Employment-Related Geographical Mobility Among Canadian Seafarers on the Great Lakes and St. Lawrence Seaway
Bibliographic record
Abstract
Seafaring involves multiple patterns of mobility. Ships are mobile workplaces that connect and disconnect from land. Many move within and between national boundaries. Maritime labor forces are recruited from multiple locations engaging in varying commutes to and from homeports—international commutes for international labor forces and internal commutes for national labor forces. Mobilities expose seafarers to a range of occupational health and safety hazards, which can be exacerbated by mobility-related constraints on regulatory protections. Based on legal analysis and twenty-five semi-structured interviews with Canadian seafarers, managers, and key informants, this exploratory study examines how employment-related geographical mobility may create occupational health and safety challenges for Canadian seafarers working on the Great Lakes and the St. Lawrence Seaway. Findings show that few legal instruments are available to protect seafarers from commuting-related occupational hazards and that occupational health and safety challenges are numerous. Seafarers’ occupational health and safety rights on board are restricted and they are systemically discouraged from raising safety concerns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 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) | 0.003 | 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".