How Good Is Good: Improved Tracking and Managing of Safety Goals, Performance Indicators, Production Targets and Significant Events Using Learning Curves
Bibliographic record
Abstract
We show a new way to track and measure safety and performance using learning curves derived on a mathematical basis. When unusual or abnormal events occur in plants and equipment, the regulator and good management practice requires they be reported, investigated, understood and rectified. In addition to reporting so-called “significant events”, both management and the regulator often set targets for individual and collective performance, which are used for both reward and criticism. For almost completely safe systems, like nuclear power plants, commercial aircraft and chemical facilities, many parameters are tracked and measured. Continuous improvement has to be demonstrated, as well as meeting reduced occurrence rates, which are set as management goals or targets. This process usually takes the form of statistics for availability of plant and equipment, forced or unplanned maintenance outage, loss of safety function, safety or procedural violations, etc. These are often rolled up into a set of so-called “Performance Indicators” as measures of how well safety and operation is being managed at a given facility. The overall operating standards of an industry are also measured. A whole discipline is formed of tracking, measuring, reporting, managing and understanding the plethora of indicators and data. Decreasing occurrence rates and meeting or exceeding goals are seen and rewarded as virtues. Managers and operators need to know how good is their safety management system that has been adopted and used (and paid for), and whether it can itself be improved. We show the importance of accumulated experience in correctly measuring and tracking the decreasing event and error rates speculating a finite minimum rate. We show that the rate of improvement constitutes a measurable “learning curve”, and the attainment of the goals and targets can be affected by the adopted measures. We examine some of the available data on significant events, reportable occurrences, and loss of availability. We suggest the use of learning curves as a means of accurately tracking progress; and stress the importance of a sustained learning environment in performance improvement.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".