Evaluation of Sustainability Reporting in the Canadian Electricity Sector
Bibliographic record
Abstract
Sustainability reports are essential platforms through which organizations communicate their corporate social responsibility (CSR) commitments to stakeholders and demonstrate accountability. Organizations often commit significant resources annually to the production of sustainability reports; however, several debates arise as to the utility and quality of information in these reports. \nIn this light, this study presents an evaluation of sustainability reporting in the Canadian Electricity Sector using the Industry’s sustainability leaders (member companies of the Canadian Electricity Association) as a case study. \nThe research adopted a mixed method approach, which consisted of two studies. The first study utilized content analysis to evaluate sustainability reports and further determine the extent to which 15 identified sustainability issues relevant to the electricity industry were addressed in the reports. The second study utilized the themes derived from the first study to construct an online survey to gain understanding of how companies perceive their report and further determine how the 15 sustainability issues ranked in order of relevance to the company’s operation. \nThe objective of this comparison was to determine if the most relevant issues to the companies (as identified by the survey) were indeed the most reported issues (as shown in the sustainability/annual reports). Results obtained revealed that the sustainability communication of the Canadian electricity association had significantly greater focus on the social aspect of sustainability than the environment and economic aspects. Furthermore, the result identified a disconnect between the most relevant issues to the companies and the most reported issues.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".