Standard‐setting institutions' user‐oriented legitimacy management strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it