An Essay on Accounting's Social Complexity and the Fairness Challenge/ESSAI SUR LA COMPLEXITÉ SOCIALE DE LA COMPTABILITÉ ET LE DÉFI DE LA FIDÉLITÉ DE L'INFORMATION
Bibliographic record
Abstract
ABSTRACT We are honoured to contribute our ideas to the tribute to Ross Skinner, stimulated by his 1995 CA Magazine article “Judgment in Jeopardy". Our effort is titled “An Essay” because we want to suggest an approach to the problem of judging fairness in financial reporting that Skinner raised and to explore some consequences of that approach. We include no literature references other than to “Judgment in Jeopardy", and occasionally to Homer, because we have developed the ideas in this essay specifically for this tribute and have not relied on others in doing so. There has been an enormous amount of writing about accounting standards, professional judgment, fairness, and other related topics, but rather than exploring all that, we have focused directly on Skinner's concerns and have tried to develop some consequences of those concerns. One of the issues Skinner's article raised is the possibility that a solution to the judgment problem will forever be elusive. Another is the enormous challenge that “fairness” presents in an increasingly complex world. To explore the implications of Skinner's call for judgment, we use a framework based on the relevance, reliability, and validity of accounting information as a means of addressing this challenge and the demands on social consensus that result. Since Skinner was concerned about the application of judgment to real problems, we illustrate our analysis by considering four contemporary accounting debates: principles versus rules, global convergence of accounting standards, the need for a stand‐alone Canadian standard‐setter, and accounting for stock options. We argue that ideological choices, competition among parties in society, and fundamental measurement problems support Skinner's prediction that a solution will remain elusive.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".