Do the Big 4 and the Second-Tier Firms Provide Audits of Similar Independence?
Bibliographic record
Abstract
In this paper, we examine the relation between the Big 4 and Second-tier auditors with auditor independence. Prior research suggests that the Big 4 audit firms are of higher independence than are non-Big 4 firms. The study also indicates a view that, both the public company respondents and audit firm respondents perceived the Big Four audit firms as having a higher auditor independence than other audit firms, which is consistent with findings of (Abu Bakar et al., 2005; DeAngelo, 1981b)Data were collected by two methods, a questionnaire survey (quantitative) and a number of semi-structured interviews (qualitative) to give both triangulation and amplification. The questionnaire was analysed using both conventional comparative statistics and multivariate methods. The sample of respondents comprised three groupings: accounts managers, financial managers and internal auditors working in Libyan public companies; managing partners, partners, audit supervisors and auditors working in audit firms in Libya; and controllers working for the Libyan Association of Auditors and Accountants (LAAA).The results of the study indicate two groups agreed that the big audit firms have enough financial resources and a large number of clients, which means they can resist client management pressure, and protect their reputation.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".