The effect of safety risk management and airport personnel competency on aviation safety performance
Bibliographic record
Abstract
Aviation safety performance at Wamena Indonesia airport is still relatively low, and this is shown by the high number of accidents that occurred at the airport in the 2013-2018 period, namely 20 accidents. The low performance of flight safety at Wamena airport is suspected to be related to the lack of optimal implementation of safety risk management and the competence of airport staff that still needs to be improved. The study aimed to determine the effect of safety risk management and airport personnel competence on flight safety performance at Wamena airport in eastern Indonesia. The research was conducted using a quantitative approach involving some respondents from airport staff and as many as 60 officers. The results showed that there was a significant direct effect of safety risk management on the flight safety performance of 79.8%; there is a significant direct effect of airport personnel competence on flight safety performance by 81.8%; there is a significant direct effect of airport personnel competence on safety risk management of 98.1%; there is a significant direct effect of safety risk management and the competence of airport personnel simultaneously on flight safety performance by 96% and based on research results show that indirectly safety risk management has an influence on flight safety performance through airport personnel competence of 97.7%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".