Rethinking Accounting, Accountability, and Accounting Regulation: Concerns about the Proposed Canadian Sustainability Standards Board*
Bibliographic record
Abstract
Abstract This paper expands on a letter recently submitted by a group of Canadian business academics to the Independent Review Committee on Standard Setting in Canada (IRCSSC) in response to the committee's proposed Canadian Sustainability Standards Board. We highlight sections of the IRCSSC's Consultation Paper that we find problematic and draw on accounting and other research to explain why it fails to live up to its potential. Chief among the problems we identify is that the IRCSSC appears to be wedded to the same narrow, investor‐based focus promoted by the International Sustainability Standard Board. We also draw attention to the rushed nature of the process, its exclusion of lay experts, the IRCSSC's ambiguous use of the term public interest, and its inattention to alternative understandings of value and the environment (including the people within it). Finally, we problematize the IRCSSC's sidestepping of the issues of power, culture, and conflict; its neglect of monitoring and enforcement; and its surprising disregard of the Global Reporting Initiative. Along with a number of suggestions for improving the process and its outcome, this paper also contributes to ongoing debates on standard setting and the question of whether accounting is currently equipped to provide the necessary tools for sustainability reporting.
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.
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 | 0.099 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.033 | 0.046 |
| Scholarly communication | 0.033 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.023 | 0.028 |
| 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".