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Record W2028436443 · doi:10.1108/17542410810858330

Gender differences in work experiences and satisfactions of Norwegian oil rig workers

2008· article· en· W2028436443 on OpenAlexaff
Ronald J. Burke, Stig Berge Matthiesen, Ståle Einarsen, Lisa Fiskenbaum, Vibeke Soiland

Bibliographic record

VenueGender in Management An International Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsYork University
Fundersnot available
KeywordsNorwegianOriginalityPerceptionWork (physics)PsychologyValue (mathematics)Job satisfactionSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Purpose The present study sets out to compare women ( N =24) and men ( N =613) working on Norwegian oil rigs in the North Sea on work experiences, work satisfaction, perception of safety attitudes and safety climate, and psychological health. Design/methodology/approach Data were collected using questionnaires from 1,022 women and men, a 59 percent response rate. Only those respondents working in traditionally male‐dominated jobs were considered. Findings Few differences were observed, suggesting that those women that continue in this occupation compare favorably with their male colleagues. Research limitations/implications The findings should be considered tentative, given the small number of women taking part in the study. Practical implications For the past three decades, women were encouraged and supported to enter non‐traditional occupations (NTOs). NTOs were occupations that have traditionally been male‐dominated. Only modest inroads have been made by females during this time. Women in NTOs typically report work experiences reflecting unique challenges, most resulting from the gender culture of their workplace and findings show that women that survive in these jobs report similar experiences to those of their male colleagues. Originality/value The paper adds to one's knowledge of women's experiences in non‐traditional jobs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.451
Teacher spread0.273 · 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 teacher head, not a consensus.

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

Citations15
Published2008
Admission routes1
Has abstractyes

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