Failing to Fix What is Found: Risk Accommodation in the Oil and Gas Industry
Bibliographic record
Abstract
The present program of research synthesizes the findings from three studies in line with two goals. First, the present research explores how the oil and gas industry is performing at risk mitigation in terms of finding and fixing errors when they occur. Second, the present research explores what factors in the work environment relate to a risk-accommodating environment. Study 1 presents a descriptive evaluation of high-consequence incidents at 34 oil and gas companies over a 12-month period (N = 873), especially in terms of those companies' effectiveness at investigating and fixing errors. The analysis found that most investigations were fair in terms of quality (mean = 75.50%), with a smaller proportion that were weak (mean = 11.40%) or strong (mean = 13.24%). Furthermore, most companies took at least one corrective action for high-consequence incidents, but few of these corrective actions were confirmed as having been completed (mean = 13.77%). In fact, most corrective actions were secondary interim administrative controls (e.g., having a safety meeting) rather than fair or strong controls (e.g., training, engineering elimination). Study 2a found that several environmental factors explain the 56.41% variance in safety, including management's disengagement from safety concerns, finding and fixing errors, safety management system effectiveness, training, employee safety, procedures, and a production-over-safety culture. Qualitative results from Study 2b suggest that a compliance-based culture of adhering to liability concerns, out-group blame, and a production-over-safety orientation may all impede safety effectiveness.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".