Bibliographic record
Abstract
Amernic and Craig (2004 ) propose the impossibility of anything other than a binary divide between moral absolutism and relativism. Campbell (1992 ) is drawn on to support this; however, it is revealed here that it cannot be used in this way. While Amernic and Craig leave the arguments as to the inevitability of this binary divide at this juncture, their discussion of the possibilities for accounting education reform offers the prospect of an alternative to relativism that avoids moral absolutism. More fortunately, Campbell's book provides the theoretical arguments necessary to rebut both absolutism and relativism. This rejoinder observes that, in reviewing the history of accounting, Amernic and Craig drew heavily on the work of Raymond Chambers, a critic of current accounting practice. In many works he demonstrated its failure to produce data comparability when complying with current accounting standards to report the values of assets, debts and income. For Chambers, truth in accounting (and ultimately data comparability) is found by the rigorous application of scientific rules. Introducing students to his persuasive arguments would be a very effective way of helping them understand the fragile and disputed nature of truth in accounting. But students should also be introduced to the arguments of those such as Gaffiikin, who believe there are limits to the extent that accounting can be based on objective facts (2000). To allow them simply to accept the orthodoxy of the day not only fails to prepare their minds ( Clarke, 1996 ), it also allows relativism into our classrooms. Students who can grapple with truth in accounting can then start addressing ethical truth. We must take our students beyond professional rules of conduct. A goal outlined in the article should be to teach them to treat the readers of financial statements with, as Campbell puts it, ‘integrity and insight’ (p. 438).
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".