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 Fears over public accounting becoming increasingly concentrated have inspired several attempts to study the relationship between competition and audit quality. These studies have yielded conflicting results without a clear reason as to why. This paper aims to propose a new approach and empirically demonstrate a non-monotonic association between competition and audit quality. Design/methodology/approach Using metropolitan statistical area level data from the USA over the period of 2000–2014, the author shows that the effect that changes in the competition will have on audit quality depends upon the current competitive state of the market. Findings Audit quality is at its highest level when competition is neither too high nor too low. In addition, the point of inflection at which competition turns from being helpful to harmful is influenced by the saturation of the Big 4 auditors in the market. Practical implications These findings can help explain the mixed results of the literature and provide insight into the role that regulators can play in modulating competition. Originality/value This is the first paper to document a non-monotonic relationship between competition and audit quality. By introducing and exploring the validity of a non-monotonic component in the audit quality equation, the authors can better determine, which competitive structures generate desired levels of audit quality.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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