Managing and predicting technological risk and human reliability: A new learning curve theory
Bibliographic record
Abstract
In the use of homotechnological systems (HTSs) over the last two centuries, literally millions and millions of data points have been amassed on human deaths, injuries, losses, damages, disasters, and tragedies. The technological risk needs to be reduced by predicting the human reliability. A fundamental theory based on the learning hypothesis has been developed to provide a sound prediction, which is consistent with the theory of error correction. The rate of reduction of the error rate with increasing experience is proportional to the rate at which errors are occurring. The risk probability for any outcome or error is derived in the form of a ‘human bathtub’ curve. The probability of an outcome as a function of accumulated experience is predicted. The measure of the risk is shown to be the information entropy, which is an objective measure of order observed in organizational learning curves, which arise from the unobserved disorder and statistical fluctuations of human reliability. Thus, the technological risk is inextricably coupled to, and interwoven with, the human reliability by a prediction methodology that is validated by data.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".