Mapping the state of expanded audit reporting: a bibliometric view
Bibliographic record
Abstract
Purpose This study aims to identify the intellectual structure of expanded audit reporting (EAR), offers a quantitative summation of prominent themes, contributors and knowledge gaps and provides suggestions for further research. Design/methodology/approach This research uses various bibliometric techniques, including co-word and co-citation analysis for EAR science mapping, based on 123 papers from Scopus Database between 1991 and 2022. Findings The results show EAR research is focused on Audit Quality; Auditor Liability and Litigation; Communicative Value and Readability; Audit Fees; and Disclosure. Regarding EAR research, Brasel et al. (2016), article is the most cited paper, Bédard J. is the most cited author, Laval University is the most influential university, The Accounting Review is the most cited journal and USA is the leading country. Furthermore, the results show that in common law countries, in which shareholder rights and litigation risk is high, topics such as disclosure quality and audit litigation have been addressed more; and in civil legal system countries, which usually favor stakeholders’ rights, topics of gender diversity or corporate governance have been more studied. Practical implications This research has practical implications for standard setters and regulators, who can identify important, overlooked and emerging issues and consider them in future policies and standards. Originality/value This paper contributes to the literature by providing a more objective and comprehensive status of the accounting research on EAR, identifying the gaps in the literature and proposing a direction for future research to continue the discussion on the value-relevance of EAR to achieve more transparency and less audit expectation gap.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.022 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.232 | 0.278 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".