Auditors’ Contribution in Enhancing Non-Quantitative Information Quality
Bibliographic record
Abstract
The purpose of this research is to determine, first, whether an auditor, by conducting a statutory audit of the company’s financial statements, can improve the company’s non-quantitative information quality, and, second, whether the six leading audit firms in Greece improve the non-quantitative information quality more than other, smaller audit firms. The data are primary, arising from the published financial statements for the period from 2019 to 2022 of 84 companies operating in the agricultural supplies sector. These financial statements were retrieved in January 2024 from the General Commercial Registry of Greece. We have reviewed the management reports of these companies to examine their compliance with Greek legislation requirements on non-quantitative information reporting, i.e., on the entity’s performance, business risk management, and environmental and labor issues. We note that non-quantitative information reporting is improving during the period 2019–2022, regardless of the auditor’s involvement. The average reporting scores of audited companies are higher than the corresponding scores of non-audited companies, so the auditors have significantly improved the non-quantitative information reporting. In addition, the reporting scores of companies audited by six leading audit firms are higher than the corresponding scores of companies audited by the other, smaller audit firms.
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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 | 0.092 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".