Going GAGAS for due process: examining Yellow Book standard participation
Bibliographic record
Abstract
Purpose The US federal government requires auditors to follow governmental auditing standards when performing audits of entities expending significant federal government dollars. This study explores stakeholder participation during the comment letter phase of government auditing standard setting to determine if participation is symbolic or substantive. Design/methodology/approach Researchers conduct an analysis of the 179 comment letters submitted to the US Government Accountability Office (GAO) and received for their 2010 and 2017 exposure drafts of government auditing standards. Findings The distribution of stakeholder participation groups in the government auditing standard-setting process differs from the distribution in the private company auditing standard-setting process. On average, participants submit letters that are greater than two pages in length. Participants also contribute feedback on topics that the GAO directly solicits. Taken together, the results demonstrate stakeholder behaviors that are consistent with a substantive rather than symbolic due process involvement for government auditing standards. Research limitations/implications Stakeholder beliefs are inferred based on the observed behavior of comment letter submissions. Also, there is a subjective element to the classification of the comment letters for the study. Practical Implications Given the far-reaching implications of Yellow Book auditing standards on public, private and nonprofit entities, the findings are relevant to a heterogeneous audience. This study reveals opportunities for users of government auditing standards, practitioners and academics for greater involvement in due process standard setting to bring additional legitimacy to the GAO and its standard-setting activities. Originality/value Beyond the current study, little empirical research examines Yellow Book auditing standards or the due process through which these standards are established. This is the first study to examine the complete set of comment letters for the 2010 and 2017 exposure drafts of government auditing standards.
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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 | 0.052 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".