La qualité de l'audit : histoire d'un concept et de son utilisation dans la recherche académique (ou l'histoire de la naissance d'une chimère)
Bibliographic record
Abstract
In 2014, the International Auditing and Assurance Standards Board (IAASB) has issued a framework for audit quality which describes the input, process, and output factors of audit quality. This document highlights the importance and the complexity of this multifaceted concept for regulators. In the last 20 years, audit quality research has become a mainstream research area and it appears that recent research often fails to reflect the multiple dimensions of audit quality. The objective of this paper is to analyze the birth and evolution of the definition and use of the concept of audit quality in academic research. To reach thos objective, we adopt a historical perspective and we investigate how published academic research has defined, measured and used ‘audit quality' over time. Based on 262 collected papers which explicitly consider audit quality as their main research topic, we observe how audit quality research has evolved over time and we attempt to identify the main contextual factors that have shaped this evolution.
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.
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 | 0.050 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".