Advancing Sustainability Reporting in Canada: 2019 Report on Progress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it