Risk Informed Decision Making by a Public Safety Regulatory Authority in Canada: A Case Study involving Risk Based Scheduling of Periodic Inspections
Bibliographic record
Abstract
The Technical Standards and Safety Authority (TSSA) is an independent, not-for-profit organization that administers and enforces public safety laws associated with Amusement Devices, Elevating Devices, Boilers and Pressure Vessels, Fuels, Operating Engineers, and Upholstery and Stuffed Articles, under Ontario’s Technical Standards and Safety Act through an administrative agreement with the province of Ontario in Canada. TSSA has introduced a risk informed decision-making framework across a wide variety of safety activities in each of the regulated sectors. One of these activities involves periodic engineering inspections of facilities and equipment during operation to ensure that the devices continue to operate safely. The inspection process reviews how the equipment and technology are being used, operated and maintained, and identifies any non-compliance to safety codes and regulations. TSSA has designed and implemented risk-based inspection scheduling models across most of its industry sectors to ensure that devices that present the highest risk to the public are inspected more often while low-risk units are assigned longer inspection cycles. The model development and implementation was carried out in three stages: 1) Concept development based on identification of risk factors representing the devices and equipment, human-device interaction, and location; 2) Model design using a semi-quantitative risk assessment approach; and 3) Implementation, including automated scheduling, monitoring, and measurement. This presentation will describe the three stages with specific reference to the model developed for the elevating devices sector.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".