Creating a Culture of Safe Driving Behaviors
Bibliographic record
Abstract
Abstract Within the oil and gas industry, and in particular Canada, driving to and from the lease site poses the greatest risk to employees. Given the variety of driving terrains, changing weather conditions, remoteness of the wellsite and driver complacency, the driving risk also represents the greatest challenge for making improvements. At Halliburton Group Canada, several approaches have been used in an attempt to reduce both the severity and frequency of motor vehicle collisions. The approach taken involved primarily behavior-based strategies, but also utilized traditional approaches to driver related behavior management such as policies and driver education. By examining the lagging indicators related to our various product service lines, we have observed a continued decrease year over year in the number of severe collisions. In 2000, Halliburton Group Canada was driving approximately 400,000 miles between serious wrecks. Today, with the systems in place, this number has significantly increased to over 2,500,000 miles. With these statistics in mind and utilizing a more traditional or compliance-based approach to driving-related issues, a decrease in the overall number of motor vehicle-related issues would also have been expected. However, with the positive behavioral approach taken, the opposite has been observed. During the same time, a significant increase in the reporting of the more minor fender benders and near misses was observed. In a completely compliance, or traditional approach, these types of incidents would have gone unreported and been dealt with on a reactive basis. This case study will provide evidence supporting our belief that creating a culture of acceptable driving behaviors through behavior-based changes can have a significant, longer-term impact that provides sustainability.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".