Bibliographic record
Abstract
Abstract How do you ingrain a culture of responsible environmental performance? First rolled out in June 2005, EMFORCE (ExxonMobil Fundamentals of Regulatory Compliance and Environment) is an in-house course that equips employees and contractor leaders with skills, knowledge and tools to lead the workforce in environmentally responsible day-to-day operations and project planning. To date, this two day course has been delivered to twenty three classes totaling over 400 workers. Typical class attendees include Steam Engineers, Operations Technical Facilities Managers, Plant Foremen, Field Foremen, Maintenance Planning Leaders, Operations Superintendents, Plant and Field Operations Specialists, Project Engineers and Advisors for whom it is required training. EMFORCE provides a foundation of knowledge from which to begin, and positions environmental responsibility as a core value. What it means for each individual to further the corporate goal of "Protect Tomorrow. Today." is explored, and the expectation that all workers can and must be Environment Leaders is woven throughout. The course culminates with a call to leadership, and the public sharing of each participant's Personal Action Plan with senior management. The ongoing sharing of lessons learned and reinforcing of messaging continues outside the course through the use of "EnviroAlert" bulletins to operating and project groups on a regular basis. EMFORCE is contributing to real environmental performance improvements including a reduction in lease sizes for new shale gas drilling and reduced night time lighting at Imperial Oil Resources’ western Canada operations to name a few. Environmental considerations are now consistently making their way into pre-job planning meetings and job safety analyses. EMFORCE is helping build a culture of responsible environmental performance. The course continues to be successful for a number of reasons. It is continually updated by senior in-house environment and regulatory staff who deliver the material. Senior management actively and visibly provides support by personally opening and closing each session. In addition, the course features recent and relevant in-house case studies, uses breakout groups, and requires each participant to develop a "Personal Action Plan". Examples of expected behavior are modeled and discussed.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".