The History of Enterprise Risk Management at Hydro One Inc.
Bibliographic record
Abstract
Hydro One Inc. is widely regarded as having had one of the most successful implementations of enterprise risk management (ERM). The purpose of this article is to record the history of this successful implementation so that it will benefit other companies and organizations who are at the beginning or in the early part of their ERM journey. In this article, we delve deeper into the dynamics at work and the steps involved in the implementation of ERM. This article is an interview by Betty Simkins with John Fraser and Rob Quail so as to record the challenges, successes, and methods used at Hydro One. This article covers the period from 1999 to when John Fraser left the ERM function in 2014 but many of the processes they implemented have continued to the date of writing (2021). This article should also be of interest to academic researchers who seek to understand why some ERM implementations succeed while other flounder or fail to achieve their objectives.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".