Advancing Sustainability Reporting in Canada: 2019 Report on Progress
Bibliographic record
Abstract
ABSTRACT This study examines the progress Canada's largest companies are making in their environmental, social, and governance (ESG) disclosures. Given the introduction of the Global Reporting Initiative (GRI) Standards and the United Nations Sustainable Development Goals (UN SDGs) as well as the issuance of the Task Force on Climate‐Related Financial Disclosures (TCFD) recommendations, our research reflects the uptake of these guidance documents by both mature and new reporters. Our analysis suggests that challenges persist—processes and progress often fail to reach investors as they are “lost in translation” when issued through third‐party ESG information providers, and reporters are also pressured to respond to a myriad of requests for information from rating and reporting agencies. Nevertheless, we note that Canada has new reporting sectors that must mature to survive the scrutiny of the markets and also hope that stock markets will respond to the recent announcement by the 181 CEOs of the U.S. Business Roundtable, who committed to lead their companies for the benefit of all stakeholders—customers, employees, suppliers, communities, and shareholders. Overall, we believe that our research will provide food for thought for companies interested in continuous improvement.
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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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".