Safety based dynamic uncertainty reduction to increase safety performance in aviation industry
Bibliographic record
Abstract
There is a lot of evidence regarding air flight accidents caused by human error, especially air traffic controllers (ATC). On the other hand, the principle of work safety through safety performance can help organizations reduce the number of work accidents and create zero accidents in the aviation industry. This research aims to analyze the effect of leadership on safety culture and safety performance by investigating the mediating role of Safety Based Dynamic Uncertainty Reduction (SDUR) as an integral aspect in safety research. A total of 214 respondents were involved in this research. The analysis technique used in this study is Partial Least Square-Structural Equation Modeling (PLS-SEM). The results showed significant effects of leadership on safety culture and safety performance. The mediating analysis also reveals the significant effects of SDUR in strengthening the impact of leadership to safety performance. As implications, SDUR can be considered as an effective strategy in improving Safety Performance in the workplace.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".