Safe Choice – Operationalizing Human Performance Science in Decision-Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An approach for enhancing safety performance in Energy Industry field applications by integrating decision-making science will be presented. Results – both qualitative and quantitative – will demonstrate step change potential in safety performance in pursuit of plateau breakthrough to zero high severity incidents. Safe Choice empowers and enables safe decision-making at all levels of an organization by providing new knowledge and techniques, and linking these to current behavioral based safety practices. Emerging understanding about brain and social science, as it relates to Energy Industry safety, is provided in practical discussion centered around decision-making. Workforce members are entrusted and empowered with new knowledge, personal decision-making style survey results, and an appreciative inquiry discussion that integrates brain science concepts in a simple effective way to their existing, familiar work processes and tools for managing safety and risk in their operating, drilling, and construction field sites. Following Safe Choice, individuals have a greater understanding of their own human performance and decision-making. Focusing on individual learning and awareness is the differentiator. The program was first developed for the ExxonMobil Hebron Project integration, hook-up and commissioning construction site in Newfoundland and Labrador, Canada during 2015-2016. Together, with other transformational safety leadership initiatives, Safe Choice contributed to best-in-class safety performance. Safe Choice was then further developed and adapted for application within operating field sites during 2017. With further success, the program is now being implemented globally with an agile, user-centered design philosophy and approach. The small group approach to training includes each worker receiving an individual decision-making style report and creates an atmosphere of appreciative inquiry, trust and openness. Developing leadership supporting strategies that foster a continuation of this atmosphere once back in the field (and outside of the classroom) has proven effective, with use of the new language and concepts evident in regular daily meetings such as toolbox talks, shift handover and safety meetings, as well as being used between workers during conversations in the field. Many locations where Safe Choice has been implemented have excellent safety performance, and will show both qualitative and quantitative measures of success achieved. Energy Industry Leaders, Operations, Drilling, Construction and Safety Professionals will gain new knowledge on successful next-step integration of decision-making science into safety programs for protecting their workforce. This will expand and extend earlier insights from panel discussions at SPE HSSE Meetings in New Orleans (April 2017) and Abu Dhabi (April 2018). This paper includes results of the program so far.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it