The Understanding about Cultural Intelligence of Cabin Crew from Thailand's International Airlines
Bibliographic record
Abstract
In the airline business, cabin crew are the employees who have direct contact with passengers. They are in an occupational group that generally has to face many difficulties in the workplace. In addition to their responsibilities to ensure the safety of the flight, one particular issue that creates tremendous stress for cabin crew is when they have to serve passengers from other different cultures. This study explores the understanding of cabin crew from the international airlines concerning cultural intelligence. The key informant of 12 cabin crew were selected from the international airlines in Thailand. Interview approach was used to collect the data using an in-depth interview form which was then analyzed by using content analysis. The findings revealed 1) there is the importance of knowing cultural intelligence for cabin crew from the international airlines, 2) it is not just the skills to deal with the passengers but everyone in workplace, 3) everyone is different, 4) their experiences matter, 5) we should think, learn, plan, and act, 6) we also should embrace and adapt the Thai cultures that have already existed to the world, 7) it is important to learn knowledge through the diversities and differences, 8) and also learn the knowledge through experiences, 9) we should realize the awareness of cultural diversity and difference. 10) we should expand the perspective since the world is bigger than you think, 11) we should discuss and learn the shared understanding and involvement in cultural diversity and difference for every cabin crew, and 12) we should identify the appropriate actions to effectively work and deal with people from different cultural backgrounds. The study summarized, then proposed the findings of overall understanding about cultural intelligence of cabin crew to Thailand’s international airlines organization. The researchers proposed the outcomes to the research institutions, the academic institutions, and the airline organizations in Thailand and around the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".