Current Human Factors Approaches in Aircraft Maintenance Sector: Transformation of Dirty Dozen into Filthy Fifteen
Bibliographic record
Abstract
In the early 1990s, aircraft accidents caused by human factors in aircraft maintenance tasks became an important agenda item in the aviation sector. In this context, as a result of studies conducted by Transport Canada employee Gordon Dupont and his teammates in 1993, twelve human factors that caused aircraft maintenance workers to make mistakes were identified, and these factors were named the Dirty Dozen. In the long process from the emergence of the Dirty Dozen model to the present day, there have been organizational and technological changes worldwide that could affect the performance of aircraft maintenance workers. For this reason, in recent years, it has been observed that some of the world's leading aircraft maintenance organizations have been using new approaches in human factors training. One of these new approaches is the Filthy Fifteen model put forward by Hawker Pacific Aerospace. However, it is seen that the studies in the literature on the Filthy Fifteen are extremely limited. In this context, the aim of this study, which is carried out theoretically, is to first examine the Dirty Dozen model, which is integrated with the concept of human factors in the aircraft maintenance sector, then to examine in detail the theoretical structure of the Filthy Fifteen model, which expands the Dirty Dozen, and to discuss the current human factors that can be added to the Filthy Fifteen model in the conclusion section. It is thought that the study will make an original contribution to the literature on human factors in the aircraft maintenance sector.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".