Standard‐setting institutions' user‐oriented legitimacy management strategies
Bibliographic record
Abstract
Purpose The objective of this paper is to critically examine the Canadian Accounting Standards Board's (AcSB) legitimacy management strategies directed toward financial statement users. Design/methodology/approach Suchman's legitimacy typology is used as a lens through which the AcSB's legitimacy management strategies directed toward users are analyzed. The data sources consist of documentary public information available for the overall Canadian standard‐setting process and for a sample of standard‐setting projects. Findings The results indicate that the AcSB devotes much more efforts to symbolic features and cultural accounts than to pragmatic concerns to ensure its legitimacy toward financial statement users. The legitimacy management strategies used mimic those in the USA and at the international level. Such an isomorphism contributes to the AcSB's cognitive legitimacy and overall cultural legitimacy. Research limitations/implications Future research could assess a standard‐setting institution legitimacy management strategies directed to other audiences such as preparers, auditors, or other groups that fall under a broader public interest umbrella. Practical implications The results provide Canadian users with a general picture of the AcSB's efforts in their regard and invite them to be sceptical and critical about the so‐called user perspective in standard setting. It also provides standard setters with a legitimacy framework that they can use to identify areas for improvement to enhance users' view of their legitimacy and to help them better fulfil their mission statement. Originality/value This paper innovates by studying a standard‐setting institution legitimacy management strategies directed toward a specific audience, financial statement users.
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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 | 0.060 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".