Human Factors and Errors in Security Aviation: An Ergonomic Perspective
Bibliographic record
Abstract
In a previous study we have identified the incidence of human factors and errors in the security screening process, concerning the Brazilian civil aviation. This finding led to the analysis of labor activity in the security checkpoint from an ergonomic perspective. The objective of this study was to evaluate the various labor conditions in the security checkpoints of Brazilian airports, in order to promote safer and higher performance of the AVSEC professionals, as well as the security equipment. For this purpose, we analyzed 14 of 60 questions of a self-observation questionnaire based on the theory of Generic Error–Modelling System (GEMS) and the four themes about human factors recommended by the International Civil Aviation Organization (ICAO). The questionnaire was responded to by 602 AVSEC professionals who worked in the security checkpoints of 18 Brazilian airports. Our analysis focused on the preponderant indexes of each question, taking into account the sequential arrangement in which the questions were displaced in the questionnaire and the classification of human factors and errors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".