Identifying key risk factors in air traffic control by exploratory and confirmatory factor analysis
Bibliographic record
Abstract
Abstract This study employs exploratory and confirmatory factor analysis to identify key risk factors in air traffic control (ATC) influencing aviation safety and to explore the correlational relationships among constructs from the perspectives of air traffic controllers. A total of 57 potential risk factors are first proposed based on the framework of SHEL, namely software, hardware, environment, and liveware, by referring to a review of the related literature and observing local issues in Taiwan. Interviews are then conducted with some 232 Taiwan air traffic controllers and supervisors. Exploratory factor analysis is first performed to determine the item‐factor assignment and develop an initially proposed framework. Next, confirmatory factor analysis is performed to test the construct validity. The correlational relationships among constructs are further investigated. The results reveal that 26 of the 57 potential risk factors studied can be characterized as key risk factors. These factors are associated with five constructs – constitutional framework, human error, system interface, external communications, and controller capabilities and physical conditions. Based on the identified key factors and the tested correlational relationships among constructs, appropriate countermeasures are proposed for mitigating ATC risks. Copyright © 2010 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".