On Commitment Toward Knowledge Templates in Global Standard Setting: The Case of the FASB‐IASB Revenue Project
Bibliographic record
Abstract
ABSTRACT This article explores commitment to knowledge templates, in this case competing measurement models, in global standard‐setting processes. In particular, I examine the positions of board members of the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB) on a proposal to use fair value accounting in the measurement of revenue. The proposal to measure revenue at fair value was deliberated between 2002 and 2008 as part of the joint revenue project of the FASB and the IASB. I analyze narratives of the board proceedings on the revenue project, which reveal the positions of board members over the life of the proposal. To make sense of these positions, I use Durocher and Gendron's (2014) framework on epistemic commitment, which speaks to one's allegiance to knowledge templates. The analysis shows that individual board member commitment to different knowledge templates is fairly static despite dynamic and contentious debate on this particular proposal. While stable, board member reactions to the proposed shift toward fair value fall into recognizable patterns showing how commitment to different templates entails prioritizing of different core principles and appeals to higher authorities. Finally, the analysis shows how commitment to knowledge templates varies depending on the professional affiliations of board members. For instance, the analysis shows relatively greater consistency of commitment between board members affiliated with academia and corporate preparers than between auditors. Overall, the study indicates the importance of micro‐level features in explaining the development of macro‐level accounting policy. These features are crucial to enhancing our broader understanding of the way in which accounting standards and rules ultimately develop.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.041 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".