Bibliographic record
Abstract
Abstract Massive data and experience exist on the rates and causes of errors and accidents in modern industrial and technological society. We have examined the available human record, and have shown the existence of learning curves, and that there is an attainable and discernible minimum or asymptotic lower bound for error rates. The major common contributor is human error, including in the operation, design, manufacturing, procedures, training, maintenance, management, and safety methodologies adopted for technological systems. To analyze error and accident rates in many diverse industries and activities, we used a combined empirical and theoretical approach. We examine the national and international reported error, incident and fatal accident rates for multiple modern technologies, including shipping losses, industrial injuries, automobile fatalities, aircraft events and fatal crashes, chemical industry accidents, train derailments and accidents, medical errors, nuclear events, and mining accidents. We selected national and worldwide data sets for time spans of up to ∼200 years, covering many millions of errors in diverse technologies. We developed and adopted a new approach using the accumulated experience; thus, we show that all the data follow universal learning curves. The vast amounts of data collected and analyzed exhibit trends consistent with the existence of a minimum error rate, and follow failure rate theory. There are potential and key practical impacts for the management of technological systems, the regulatory practices for complex technological processes, the assignment of liability and blame, the assessment of risk, and for the reporting and prediction of errors and accident rates. The results are of fundamental importance to society as we adopt, manage, and use modern technology. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 279–291, 2003.
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.
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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".