Construction of The Professional Competency Evaluation Index System for Flight Attendant Specialty in Chinese Higher Vocational Education
Bibliographic record
Abstract
This study aims to build a comprehensive and scientific Chinese high-vocational airliner professional ability evaluation index system to solve the challenges and needs of the current evaluation system. Based on existing research, this study is based on the understanding and application of the theory of occupational ability evaluation, combined with the DACUM method to conduct initial occupational analysis, use of the Delphi method to integrate the feedback of experts in the field of flight attendants and refer to the successful experience of the relevant flight attendants' vocational capabilities. This study built a framework for the evaluation index system. First, through the optimization and adjustment of the two-round Delphi method, the evaluation index system was clarified, the layer analysis method (AHP) was used for quantitative analysis, the weight of each indicator was finally determined, and a hierarchical structure model was constructed. This study has established a practical evaluation index system for China Airlines' vocational professional capability, covering output services, navigation services, reactions, and other necessary capabilities and qualities, including 4 First-level indicators, 12 second-level indicators, And 51 Third-level indicators. The evaluation indicators constructed by the Institute provide a scientific reference framework for training the professional competency of flight attendants. This study provides substantial guidance for the flight attendant specialty's training content and training scheme in Chinese higher vocational education. Efficiency, reduce training costs, and provide scientific reference for the talent training of the entire flight attendant industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".