A Case for International Financial Reporting Standard on Sustainability: A Critical Perspective
Bibliographic record
Abstract
The domain of sustainability reporting has seen a substantial proliferation in recent years. Many standards, guidelines, and voluntary regulatory mechanisms have emerged, yet a specific IFRS on sustainability doesn’t exist. This paper provides a critical analysis of desirability for the institutionalization of sustainability reporting in a form of IFRS standard. First, I discuss strengths and weaknesses of IFRS and their adoption. Next, I delineate the complex multi-dimensional nature of accounting harmonization and specifically highlight the existing barriers to the successful advancement of this process. I provide a justification why a complete convergence of national accounting standards is unlikely and even undesirable, given the diversity of cultures, enforcement mechanisms, tax and legal systems around the world. In the final section I review the concept of sustainability in its breadth and depth, and analyze how the usage of analogy to financial reporting may impose constraints on the scope of sustainability development goals. By contrast to financial reporting, sustainability reporting is addressed to a much wider group of stakeholders whose engagement is crucial for meaningful sustainable development goals. The paradigm of the IFRS does not render possible embracingthis wider scope, therefore, their applicability for the purposes of sustainability reporting is limited.
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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 | 0.131 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.034 | 0.046 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".