Accelerating Industry Performance Through Collaborative Continual Improvement
Bibliographic record
Abstract
CEPA Integrity First® (Integrity First), led by the Canadian Energy Pipeline Association (CEPA) and a condition of membership, acts as a foundation for continual improvement, bringing our members together to share and implement leading practices in the areas of safety, environment and socio-economics. Integrity First includes three principles and ten priority areas (such as emergency management, pipeline integrity and water protection) where members collaborate, share leading practices and hold each other accountable. Integrity First is a management systems approach designed by CEPA members for industry to achieve collaborative continual improvement. It supports the collective setting of priorities, plans, assessments and improvements. While spreadsheets enabled the first rounds of assessments, CEPA required a solution that engaged multiple stakeholders over a complex timeline, coordinated activities clearly and precisely, while keeping the process transparent and efficient. The information generated is sensitive, so it must be kept secure while still being available for aggregation, reporting and reference. It needed to house communication tools so members could easily pull information and lastly, it needed to be easy to use. In August of 2015, CEPA established a partnership with SPAN Consulting (SPAN) to address these challenges through its software as a service (SaaS) offering called Octane™. This paper will review how CEPA designed and implemented a technical, web-based solution to enable an efficient, effective and transparent Integrity First with transformative impact. Specifically, through the use of this technology, there are now stronger communities of practice across industry with increased focus and effort on the opportunities to improve through real-time self-serve access to industry’s overall benchmarked performance, leadership and leading practices. CEPA’s commitment to enabling Integrity First is resulting in better adoption and improved performance.
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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 | 0.073 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".