From operational hazards to organizational weaknesses: Changing the focus for improvement
Bibliographic record
Abstract
The daily operations in the mining industry are still a significant source of risk with regard to occupational safety and health (OS&H). Various research studies and statistical data world-wide show that the number of serious injuries and fatalities still remains high despite substantial efforts the industry has put in recent years in decreasing those numbers. This paper argues that the next level of safety performance will have to consider a transition from coping solely with workplace dangers, to a more systemic model taking organizational risks in consideration. In this particular aspect, lessons learned from the nuclear industry may be useful, as organizational learning processes are more universal than the technology in which they are used. With the notable exception of major accidents, organizational performance has not received all the attention it deserved. A key element for reaching the next level of performance is to include organizational factors in low level events analyses, and approach management as a risk control system. These factors will then appear not only in event analysis, but in supervision activities, audits, change management and the like.Many recent event analyses across various industries have shown that organizational factors play a key role in creating conditions for triggering major accidents (aviation, railway transportation, nuclear industry, oil exploitation, mining etc.). In the following paper, we will present a perspective that may be used in supervisory activities, self-assessments and minor events investigations. When ingrained in an organizational culture, this perspective has the highest potential for continuous safety 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 | 0.001 | 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 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".