On the Over-Emphasis of Human `Error' as a Cause of Aviation Accidents: `Systemic Failures' and `Human Error' in US NTSB and Canadian TSB Aviation Reports 1996-2003
Bibliographic record
Abstract
It has been claimed that up to 80% of all aviation accidents are attributed to human `error' (Johnson, 2003). This has important consequences as national and international initiatives focus on the reduction of operator `error', for instance by increasing the levels of automation in Air Traffic Management. However, it is difficult to validate claims about the frequency of human `error'. This paper describes the results of an independent analysis of the probable and contributory causes of aviation accidents in both the United States and Canada between 1996 and 2003. The purpose of the study was to assess the comparative frequency of a range of causal factors in the reporting of these adverse events. Our results show that for the United States, organizational issues appeared as a more prominent cause of aviation accidents than individual human error. The study did reveal that individual error was the most frequent cause cited in Canadian reports. However, a large number of reports also mentioned wider systemic issues, including the managerial and regulatory context of aviation operations. These wider issues are more likely to appear as contributory rather than primary causes in this set of accident reports. An important caveat is that we are looking at the product of incident investigations rather than the processes that led to them. Further work is required to analyze the manner in which the use of investigatory techniques can influence the outcomes of particular agency's investigations. Such research would help determine whether, for example, the Canadian Transportation Safety Board's use of the ISIM approach directly helps to explain differences in their causal analysis when -2- compared to the outcomes derived from the less formal approach exploited by the US National...
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| 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".