Bibliographic record
Abstract
We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data. Comparisons with outcome error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator transient control behaviour, show a reasonable level of accord. The results demonstrate that the risk is dynamic, and that it may be predicted using the MERE learning hypothesis and the minimum failure rate, and can be utilized for predictive risk analysis purposes. 1. The risk prediction purpose Modern technological systems fail, sometimes with catastrophic consequences, sometimes just everyday injuries and deaths. The risk is given by the probability of failure, error or more generally any outcome. Recently the crash of the NASA Space Shuttle Columbia, the great blackout of the North East USA and Canada, the explosion at the Texas City refinery all occurred. Other smaller but also key accidents have also occurred: the midair collision over Europe of two aircraft carrying the latest collision avoidance system; the glider landing of a jet aircraft out of fuel in the Azores; a concrete highway overpass collapsing in Laval, Quebec; the huge oil tank fire in England; more ships sinking, more trains derailing, even more cars colliding, and evermore medical errors. We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts as in [5]. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".