Should You Offer a Job to Your External Auditor?
Bibliographic record
Abstract
This article discusses the significance of external auditor. Companies searching for financial and accounting talent to join their senior executive teams often lure partners and other professional staff away from accounting firms that perform their annual audits. Livent Inc., the Toronto-based producer of Broadway shows, recently faced this exact problem when a former professional of its external audit firm joined Livent as a member of the financial executive team, and allegedly became enmeshed in a financial reporting fraud.Although there are risks associated with hiring an individual from the external audit firm's staff, companies routinely hire ex-auditors to fill numerous types of company positions. Most of these employment relationships benefit both the company and the ex-auditor, with no decline in the quality of financial reporting. Clients often prefer to hire former employees of their audit firm because former auditors typically possess numerous attractive attributes. The level of training in an audit firm, and exposure to both numerous types of businesses and to complex accounting and financial transactions provide valuable experience.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.040 |
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".