Preparers’ opposition to proposed standards: the ‘standard-setting defects’ argument
Bibliographic record
Abstract
We examine how constituents strategically frame their opposition to proposed accounting standards, specifically in the context of preparers’ opposition to the one-statement presentation format for other comprehensive income (OCI). We use the notion of frame resonance to interpret their opposition expressed in comment letters sent in response to three OCI-related proposed standards. We find that preparers mobilised what we call ‘standard-setting defects’ arguments to oppose the requirement to use the one-statement format and convince the IASB to allow the use of the two-statement approach. Economic consequence arguments do not prevail as preparers hold a mirror to the IASB, asserting that the one-statement approach breaks from current understandings, does not meet users’ needs, arises without due process, and is misaligned with the spirit of the conceptual framework. Mobilising ‘standard-setting defects’ is not meant to inform the standard setter of genuinely held beliefs but is more intended to resonate with the IASB. This finding offers a novel way to make sense of communications in standard-setting debates, viewing the consultation process as an arena where constituents problematise the very raison d’être of standard setters by criticising them for not conforming to their own mission, conceptual views, legitimate procedures and societal expectations.
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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 | 0.045 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".