The Integration of High Performance Work Systems and Workplace Safety in the Oil and Gas Industry
Bibliographic record
Abstract
Abstract The oil and gas industry is a high risk environment. Despite advancements in technology and safety management practices, major disasters do occur and relatively minor safety related incidents occur fairly frequently. When managing safety, placing an emphasis on engineering technologies, the most proximal factor in the chain of events, or safety management practices themselves, in isolation from the complex interaction between technology, human factors and organizational structures, is insufficient and can be misleading. This paper addresses the development and integration of a high performance work systems (HPWS) approach with conventional engineering and management practices to improve safety in the offshore oil and gas industry. A safety-related HPWS approach emphasizes the role of integrating practical related engineering management and human resource management practices to produce positive outcomes and to promote a safe working environment. The integration of technical engineering practice with management systems will promote the development of an effective framework to improve workplace safety in the offshore oil and gas industry. Towards this, authors are developing an integrated approach of workplace safety management by linking predtive accident models with HPWS. This has the potential to reduce risk, enhance safety and provide a greater return on investment for this industry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".