Bibliographic record
Abstract
This thesis examines management cognition of climate risks in the electricity sector in Ontario (Canada).Risk perception literature is combined with corporate adaptation and risk management literature to offer a broad conceptual framework of climate risk readiness among power producers and utilities. This research aims to move management cognition of climate change past prior contributions which considered climate risk as being solely physical in nature. In this work, eight exogenous and endogenous factors relating to climate risk are examined for their influence on how management may view a wider spectrum of climate change impacts. Using an inductive research approach, 20 in depth case studies explore how electricity executives/senior managers perceive those risks using construct elicitation (repertory grid technique). Findings are triangulated with a narrative analysis of their corporate reportage of climate risks, to gain deeper insight into the complex phenomena of climate risks for the sector.Findings show some similarities and some appreciable differences in both groups’ view of climate risks despite their legitimately contending positions in industry. Overall both power producers and utilities are predominantly concerned with risk analysis and assessment of climate related risks, and less with risk response, suggesting at present the sector remains in an analytical state. The potential benefits of this research approach will provide useful insights to multiple groups including managers and policy makers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".