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Record W3216391191

선원의 안전보건법제 현황 및 개선방안 연구

2021· article· ko· W3216391191 on OpenAlexaboutno aff
두현욱, 이윤철

Bibliographic record

Venue海事法硏究 · 2021
Typearticle
Languageko
FieldEngineering
TopicMarine and Coastal Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationOccupational safety and healthEffective safety trainingLegislatureEnvironmental healthBusinessOccupational health nursingLawPolitical scienceMedicineHealth policyHealth care
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to review the Occupational Safety and Health legislations applied to seafarer and to suggest improvement measures for the Occupational Safety and Health Act and the Seafarers Act. The methodology of this study is a historical review of the relationship between the Seafarers Act and the Occupational Safety and Health Act through the transitional history of the safety and health legislation. Also it considered whether the Occupational Safety and Health Act is applicable to seafarers subject to the Seafarers Act. In addition, through the analysis of occupational safety and health legislations in the U.K, Canada and Japan, what kind of legislative approach has been taken are surveyed. As a result, since the Occupational Safety and Health Act does not explicitly exclude the application to seafarers subject to the Seafarers Act, there is controversy over the interpretation of the law. Furthermore, the Seafarers Act needs to provide a legal basis for the implementation of the Maritime Labour Convention, 2006 as amended. and strengthening the safety and health of the cadet in the Seafarers Act. In the long term, the legislation concerning Seafarers Safety and Health Act would be required because there is a legislative limit to strengthen and implement occupational safety and health on board only with partial amendment of the Seafarers Act.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

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