Bibliographic record
Abstract
Golden safety rules (GSR) have been in existence for decades across multiple industry sectors – championed by oil and gas – and there is a belief that they have been effective in keeping workers safe. As safety programs advance in the oil and gas sector, can we be sure that GSR have a continued role? ERM surveyed companies across mining, power, rail, construction, manufacturing, chemicals and oil and gas, to examine the latest thinking about GSR challenges and successes. As we embarked on the survey, the level of interest was palpable; from power to mining it was apparent that companies were in the process of reviewing and overhauling their use of GSR. The paper will present key insights from the survey around the questions we postulated. Are GSR associated with a punitive safety culture, and have they outlived their usefulness as company safety cultures mature? Is the role of GSR being displaced as critical control management reaches new pinnacles? Do we comply with our GSR, and how do we know? Do our GSR continue to address the major hazards that our personnel are most at risk from? How do we apply our GSR with contractors, and to what extent do our contractors benefit from that? The paper concludes with some observations of how developments outside of the oil and gas sector provide meaningful considerations for the content and application of GSR for oil and gas companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".