Emerging tools for evaluating safety management systems effectiveness
Bibliographic record
Abstract
Safety Management Systems (SMS) have become prevalent in a host of industries, including aviation, for managing safety, but little research has been performed to-date on measuring the effectiveness of SMS.This research examined the independent application of two related concepts to assess effectiveness: IO/SMS, an Input-Output economics concept applied to SMS, and Data Envelopment Analysis (DEA).Input-Output (IO) is a method for systematically determining the inter-relationships among elements in a system.To determine if IO could be applied to SMS, it was necessary to calculate the relative importance to the system of the four components of SMS.Five SMS experts participated and, through a series of exercises, determined values for the 24 discrete SMS parts.Using IO matrix math, these values were then calculated for a 24×24 matrix.The results produced a matrix that could be used to predict the impact on the system by changing either a total input value such as an aggregate score on a survey, or by changing a single value.DEA is a multi-factor, mathematical programming technique that is used to determine the boundary of an efficient frontier.Using inputs and outputs, a ratio is calculated, which measures the relative efficiency, or effectiveness, of each decision making unit (DMU).In this research, inputs and outputs were determined for each of the four components of SMS via surveys conducted by subject matter experts.DEA models were developed and tested, and efficiency scores were developed for each DMU.DEA modeling also revealed the specific areas that could be addressed to improve efficiency scores.IO/SMS and DEA appear to be powerful tools to measure SMS effectiveness.A next step in the research may be to examine techniques that combine the benefits of both methods.
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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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".