Bibliographic record
Abstract
ABSTRACT The rules versus principles debate and the vital importance of context ‐ the circumstances‐specific nature of judgment ‐ are at the heart of Ross Skinner's suggestion for an “interpretation panel". International considerations and developments involving governance and regulation have created imbalances in power, expertise, and impartiality, increasing the importance of and need for such a panel. This analysis considers the nature of the problem, how professional judgment has been characterized, and why a panel would be appropriate to address, among other concerns, the audit committee's dilemma when accounting disputes arise. Evidence is provided that management turnover is higher in cases involving multiple restatements, governance problems, or regulators' sanctions. Although, intuitively, management turnover is likely to be associated with widely publicized restatements, some patterns suggest that it is a function of entity size, scope of management changes considered, and the manner in which the restatement was identified. Specifically, an identifiable source of discovery, as well as external involvement, is associated with a greater propensity for management change. In contrast, restatements linked to changes in available guidance from regulators are less likely to result in such turnover. One implication is that effective control design and monitoring to facilitate internal discovery of errors can decrease the likelihood of multiple restatements and reduce fault finding that leads to management change. The judgmental nature of restatements suggests that an infrastructure supporting “right‐mindedness” does have merit. An interpretation panel would increase the feasibility of principles‐based standards, facilitating timely resolution of accounting‐associated disputes and thereby enhancing the information environment underlying the allocation of capital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".