The Challenge of Integrating OHS into Industrial Project Risk Management: Proposal of a Methodological Approach to Guide Future Research (Case of Mining Projects in Quebec, Canada)
Bibliographic record
Abstract
Although risk management tools are put to good use in many industrial sectors, some large projects have been met with numerous problems due to failure to take occupational health and safety (OHS) into consideration. In spite of the high level of risk and uncertainty associated with many industrial projects, the number of studies of methods for managing all known risks systematically remains small. Under effervescent economic conditions, industries must meet several challenges associated with frequent project start-ups. In highly complex and uncertain environments, rigorous management of risk remains indispensable for avoiding threats to the success of projects. Many businesses seek continually to create and improve integrated approaches to risk management. This article puts into perspective the complexity of the challenge of integrating OHS into industrial project risk management. A conceptual and methodological approach is proposed to guide future research focused on meeting this challenge. The approach is based on applying multi-disciplinary research modes to a complex industrial context in order to identify all scenarios likely to contain threats to humans or the environment. A case study is used to illustrate the potential of the proposed approach for application and its contribution to meeting the challenge of taking OHS into consideration. On-site researchers were able to develop a new approach that helped two mining companies in Quebec (Canada) to achieve successful integration of OHS into expansion projects.
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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 | 0.083 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".