A Canadian Response to the Pursuit of Global Sustainability Reporting Standards*
Bibliographic record
Abstract
ABSTRACT As business, government, and society continue to emphasize the importance of sustainability—both of individual organizations and of the natural world—accounting standard‐setting bodies want to be part of the process. In September 2020, in response to “urgent and growing demand” for more “consistent and comparable” sustainability reporting, the IFRS Foundation (the Foundation) released for comment a Consultation Paper on Sustainability Reporting. In the paper, the Foundation proposed the creation of a Sustainability Standards Board (SSB), which it would oversee alongside the IASB. The SSB would become, de facto, the global sustainability reporting standard setter. The Foundation received 577 responses to its proposal. These responses came from around the world and from a wide range of stakeholders. Thirty‐eight of the responses came from Canada. This study profiles the Canadian responses, contrasting them with the wider set of worldwide responses. Some uniquely Canadian features include user responses from a large number of pension funds and preparer responses solely from the energy sector. There was also a significant response from the Canadian accounting community, including from the full set of Canadian standard‐setting organizations. Five auditors general also responded. Overall, Canadian respondents supported both globalized sustainability reporting standards and the Foundation's creation of the SSB. This support aligns Canada's response with the worldwide response.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".