Fair Value Information, Audit fees and Audit Committee in Taiwan
Bibliographic record
Abstract
Regulatory requirements to adopt IFRS and to disclose audit fees make it possible to examine association between audit fees and proportion of fair-valued assets among firms in Taiwan. A voluntary choice of adding audit committee in the firm for monitoring purpose also helps to examine the association further. Empirical results indicate that lower audit fees is related to higher proportion of (Level 2) fair-valued assets, a finding consistent to Goncharov et al.’s (2014) suggestion that firms pay lower audit fees with fair-value model than with cost model. Insignificant association is found for proportion of Level 3 fair-valued assets, which is similar to Glover et al.’s (2014) suggestion that firm’s reluctant attitude in adopting Level 3 assets. Last of all, when audit committee is added, firm’s audit fees is negatively associated with Level 1 and 2 fair-valued assets, implying audit committee’s role of monitoring and further reducing audit risk and audit fees among Taiwanese 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".