Possibilistic regression analysis of influential factors in the planning and implementation of occupational health and safety management systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The code of Occupational Health and Safety (OHS) is an important regulation to improve the on-the-job safety of employees. Several factors affect the planning and implementation of OHS management systems (OHSMS). The evaluation of OHS practice is the most important component when building a safety environment policy for employees and administration. Begin aware of subjective nature of factors affecting OHS and the use of statistical method, it becomes controversial as to a way of handling this type of survey data. This research presents a combination of possibilistic regression analysis with a convex hull approach to analyze the fitting factors that impact good practices of OHS. In addition, selected samples of data could be represented as fuzzy sets. This study offers an alternative platform to evaluate influential factors being used towards a successful implementation of the OHS policy.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it