Cross-National Differences in Aviation Safety Records
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data about the mortality risk of scheduled passenger air travel over 2000–2007 around the world is examined in this paper. Worldwide, the average passenger death risk per scheduled flight over 2000–2007 was about one in 3.0 million. However, much as the center of mass of a doughnut is the center of the hole—where there is no mass—the worldwide average represents the actual risk level in few if any countries. The data support a three-population risk model across nations, in which the differences in death risk are not statistically significant within groups but are highly significant across groups. The safest nations are the traditional first-world countries (e.g., Canada, Japan), with a death risk per flight of about 1 in 14 million. Next safest are those developing-world nations that have either have recently attained first-world status (e.g., Singapore, South Korea) or are classified by experts as newly industrialized (e.g., Brazil, China) Their aggregrate death risk per flight was about 1 in 2 million. The least safe nations statistically are remaining developing-world countries, with a death risk per flight of about 1 in 800,000. In terms of relative risk, divergences within the developing world are modest compared to the overall difference between the first and developing worlds. The observed risk pattern might reflect a confluence of economic and cultural factors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it