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Record W3048414606 · doi:10.1007/978-3-030-44975-9_10

Mapping the Maritime Occupational Health and Safety Challenges Faced by Canadian Seafarers

2020· book-chapter· en· W3048414606 on OpenAlexafffundabout
Desai Shan

Bibliographic record

VenueSpringer polar sciences · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaOcean Frontier Institute
KeywordsOccupational safety and healthMaritime safetyWork (physics)The arcticMaritime industryArcticClimate changeBusinessPolitical scienceEnvironmental planningEngineeringGeographyLawInternational trade

Abstract

fetched live from OpenAlex

This chapter explores the occupational health and safety challenges faced by Canadian seafarers. Maritime occupations continue to be among the most dangerous occupations in the world. Technological development and climate change, as well as the increasing level of Arctic shipping opening driven by oceanographic changes together with technological innovation, lead to significant health and safety challenges for mariners in Canada. Drawing on findings from two research projects on seafaring occupational health and safety (OHS), including qualitative semi-structured interviews with 25 Canadian seafarers and a preliminary legal review of Canadian maritime OHS law, this chapter presents some common OHS challenges confronted by Canadian seafarers and the gaps existing in the current Canadian maritime OHS law. These challenges include increasing Arctic shipping activities led by the climate change, intensified work-related mobility, and insufficient legal protection.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.012
Science and technology studies0.0120.002
Scholarly communication0.0070.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.037
GPT teacher head0.229
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes3
Has abstractyes

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