Enhancing Value for Canadian Organizations by Using Enterprise Risk Management as a Holistic Approach for Improving Environmental Management and Compliance
Bibliographic record
Abstract
Environmental risks are a cumbersome financial burden to any organization. This thesis aims to establish best practices for integrating environmental risks into the ERM holistic approach to creating value for Canadian organizations as strategic speculative risk. Culture, leadership, risk appetite, integrated risk framework and value are five pillars that may influence the integration process. The research involved four phases, including a review of the literature methodologies to determine the most appropriate approach to conduct the thesis, a scoping review of 54 academic articles, a content analysis of 20 sustainability reports for leading Canadian organizations. Finally, merging the results of both literature analyses generated a best practice list, which was analyzed against the research's five pillars. The thesis concluded the organizational culture supremacy on the overall integration process and reframing the environmental risks as a rewarded risk embraced with the ERM system, thus creating stakeholder value and enhancing business continuity.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".