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Record W2418134004 · doi:10.1177/0143831x16645200

Comparing labour and management risk perceptions of offshore helicopter safety: Gaps, shifts and worker participation

2016· article· en· W2418134004 on OpenAlexaff
Susan Hart

Bibliographic record

VenueEconomic and Industrial Democracy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDocumentationReputationPerceptionSafety management systemsCrashBusinessOccupational safety and healthFocus groupRisk managementPublic relationsEngineeringOperations managementManagement systemPsychologyMarketingPolitical scienceFinanceComputer scienceLaw

Abstract

fetched live from OpenAlex

This qualitative case study compares labour and management risk perceptions of offshore helicopter transportation and worker participation in general and in two specific safety issues: survival suits and night flights. Verbatim transcripts of the parties’ testimony in a public inquiry into a fatal helicopter crash in 2009 were analysed with supplementary documentation. A persistent but shifting perceptual gap was found with limited worker and union involvement despite the accuracy of their perceptions. Potential explanations for this overall pattern focus on the social construction of risk: fateful moments, power differentiation, safety management systems, and balancing of production, safety and corporate reputation.

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)0.012
metaresearch head score (Gemma)0.031
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0020.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.093
GPT teacher head0.398
Teacher spread0.305 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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