Mapping the state of expanded audit reporting: a bibliometric view
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 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.
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.
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 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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.122 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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