The Experience and Performance of Female Airline Pilots in Taiwan - A Tripartite Assessment
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".