Independence of Aviation Safety Investigation Authorities: Keeping the Foxes from the Henhouse
Bibliographic record
Abstract
A MONG THE MOST important means of improving safety is to objectively determine the causes of aviation accidents so that appropriate action can be taken to prevent similar events from recurring in the future.The determination of causation can have an adverse political, economic, punitive, and reputational effect upon individuals, airlines, manufacturers, air navigation service providers, airports, maintenance companies, and governmental institutions.Hence, many institutions and individuals are motivated to try to influence the outcome of the investigation.Article 26 of the Chicago Convention requires a State in which an aviation accident occurs (involving death or serious injury, or involving a serious technical defect in the aircraft or air navigation facilities) to investigate the event.The Chicago Convention obliges the 190 ratifying States to implement the * This study parallels a similar evaluation by the McGill University Centre for Research on Air & Space Law and the International Civil Aviation Organization (ICAO) on governance structures of Air Navigation Service Providers (ANSPs), of which this author was the principal investigator: McGill University/ICAO, Air Navigation: Fying Through Congested Skies (Paul Dempsey, ed., McGill/ICAO 2007).That study focused on governance structures of ANSPs in ten States.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".