Review of Truth or Profit: The Ethics and Business of Public Accounting by Dean Neu and Duncan Green
Bibliographic record
Abstract
ABSTRACT Truth or Profit: The Ethics and Business of Public Accounting, by Dean Neu and Duncan Green, addresses timely issues from an original and creative perspective. The subtitle of the book describes its theme ‐ namely, the tension between the business and professional “calling” of the public accounting profession in Canada. It tracks the history of the public accounting profession in Canada from its inception until 2006, often through the eyes of the editor of the principal contemporary professional accounting magazine/journal. A subplot is that of the “normal accident", a term that is used to describe both the inevitability of this tension and the crises it produces from time to time. This work, slim (164 pages) and affordable, would be a useful resource for an upper‐level course that considers accounting ethics. Dans leur ouvrage Truth or Profit: The Ethics and Business of Public Accounting, Dean Neu et Duncan Green se penchent sur les questions de l'heure sous un angle original et créatif. Le sous‐titre du volume en indique le thème: celui de la tension entre les exigences commerciales de l'expertise comptable et ses exigences professionnelles, au Canada. Les auteurs relatent l'histoire de la profession d'expertise comptable au Canada, de sa création jusqu'en 2006, souvent à travers les yeux de l'éditeur du principal magazine contemporain sur la profession comptable. L'ouvrage traite, au second plan, de l'«accident normal», expression utilisée pour décrire à la fois le caractère inévitable de cette tension et les crises qu'elle provoque, à intervalle. L'ouvrage, succinct (164 pages) et abordable, peut être une ressource utile dans le cadre d'un cours de niveau supérieur portant sur l'éthique de la comptabilité.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".