The Rise and Evolution of the Chief Risk Officer: Enterprise Risk Management at Hydro One
Bibliographic record
Abstract
This article describes the five-year implementation of enterprise risk management at Hydro One, a Canadian electric utility in a newly deregulated market. Starting with the creation of the position of Chief Risk Officer and the implementation of a pilot risk study involving one of the firm's subsidiaries, the ERM process has made use of a variety of tools and techniques, including the “Delphi Method,” risk trends, risk tolerances, and risk rankings. Among the most tangible benefits of ERM at Hydro One are (1) a better coordinated and more effective process for allocating capital and (2) a favorable reaction to the program by Moody's and Standard & Poor's, which has arguably improved the company's credit rating and lowered its cost of capital. But perhaps equally important is the company's progress in realizing the first principle of its ERM policy—namely, that “risk management is everyone's responsibility, from the Board of Directors to individual employees.” As a result, Hydro One's management feels that the company is much better positioned today to respond to new business developments than it was five years ago.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".