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Record W4308764906 · doi:10.5539/ibr.v15n12p27

The Experience and Performance of Female Airline Pilots in Taiwan - A Tripartite Assessment

2022· article· en· W4308764906 on OpenAlexvenueno aff
Fang Yuan Chen, Ting Jyun Jheng, Daniel Liu

Bibliographic record

VenueInternational Business Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
FundersMinistry of Science and Technology, Taiwan
KeywordsCockpitCrewCrew resource managementAviationPerspective (graphical)AeronauticsPsychologyAviation safetyApplied psychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

The pilot profession remains one of the most notable gender-imbalanced occupations in the aviation industry, with women making up a far lower percentage than men. Nevertheless, the experiences and challenges faced by female pilots in the workplace is worth exploring. Previous studies have mostly approached these issues from the perspective of male and/or female pilots, ignoring the opinions of managers responsible for flight operations. This study fills this research gap by adding flight operations managers' assessments of female pilots to explore the topic from a broader and comprehensive tripartite perspective. In addition, gender issues in the cockpit and the impact of female pilot participation are also discussed. The research is conducted through in-depth interviews covering flight operations managers, male pilots, and female pilots in three different Taiwanese airlines. The findings of this study reveal that the performance of female pilots is generally affirmed by flight operations managers and male pilots, and the participation of female pilots produces some chemical effects on the culture of the cockpit, which also contributes to crew resource management and flight safety. Contrary to previous research findings, the female pilots interviewed in this study do not feel that they are being challenged or abused in relation to their gender. Finally, several recommendations are given to the airlines to implement CRM training programs and recruit female pilots.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

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

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 head0.122
GPT teacher head0.373
Teacher spread0.251 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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