Researching the Satisfaction Levels of Passengers for Security Services at Airports
Bibliographic record
Abstract
The rapid development in international trade has directly affected the air transport industry. Therefore, the number of passengers and the amount of cargo carried are increasing every year. Especially for long distance travels, passengers tend to prefer air transport. So, effective security applications at airports such as security processes, the competence of the technology used and security staff are extremely important. At the same time, having good performance of security systems may also affect the passengers’ satisfaction. In this study, the effect of security practices at airports on passengers’ satisfaction was investigated. Within the scope of the study, 536 questionnaires were applied both face to face and online to the passengers using Turkish airports. The data were tested using T-Test, ANOVA and Regression analysis. According to the analysis results, it has been found out that there is a meaningful relationship between the evaluations of the passengers towards security services at the airports and their satisfaction level. Also, evaluations of the passengers towards security services at the airports and their satisfaction level differ according to the flight frequency. Finally, some suggestions have been made to the sector administrators related to the security practices at airports in order to increase the passengers’ satisfaction level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".