Bibliographic record
Abstract
A review of the literature in the area of audit fee determinants includes fifty‐six studies drawn from seventeen countries over the period 1980 to 2000. The review starts with work initially based in the US market and then shows that attention spread almost immediately to a number of other markets, some of which were similar in structure to that of the US including the United Kingdom, Australia, Canada, India, New Zealand and Ireland. A second extension of this work has seen studies based on data drawn from markets including Pakistan, Bangladesh, Malaysia, Singapore, Hong Kong, Japan, South Korea, South Africa, The Netherlands and Norway. The comparative, analytical review highlights the use of a core audit fee determinants model that is used and adapted in a limited way, to reflect market specific circumstances and to address market specific issues. The review indicates some consistency across markets in respect of generic variables identified as core determinants of the level of audit fees. There is little evidence in the literature to indicate historical, cultural, institutional or other market‐specific factors being addressed in a systematic way, particularly in respect of developing countries.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".