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Work, Gender, and Sexual Harassment on the Frontlines of Commercial Travel: A Cross-Sectional Study of Flight Crew Well-Being

2020· article· en· W6902081298 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentOffensiveCrewWorkforceReproductive healthService (business)

Abstract

fetched live from OpenAlex

The scale and scope of experience of sexual harassment at work among male and female flight attendants was examined. Flight attendants are a predominantly female workforce with an occupational heritage of female and male sexualized stereotypes. Data represented perception and prevalence of sexual harassment related to hostile work environment among 8,700 North American (U.S. and Canada) and 1,887 United Kingdom (UK) flight attendants in the Flight Attendant Health Study. Sexual harassment, mostly from passengers, pilots, and coworkers, was reported by 26% of North American flight attendants and 11% of UK flight attendants. Yet, 61% of UK flight attendants experienced unwanted sexual behaviors, pointing to possible underreporting of the sexual harassment problem. Sexually offensive behaviors received variable labeling as sexual harassment depending on gender of the victim and perpetrator profile. Sexual harassment is a potentially underreported workplace problem for flight attendants with underreporting resulting from gender-specific differences in individual perceptions. Understanding these differences is vital to addressing sexual harassment and the concomitant health risks. Traditional client and customer attitudes might lead to rose-colored victims’ judgment, especially in the case of customer service jobs involving emotional labor. Our results provide new information to guide future research regarding well-being of this understudied group of service employees.

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)0.000
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 score0.325
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.183
GPT teacher head0.376
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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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