Bibliographic record
Abstract
This paper is a follow-up of the article ‘Fudged Accounting Theory: Evidence from the UK’ in the Journal of Management Research (Ong, 2003). In that article, an analysis of the flexibility within the UK regulations, which allowed companies to use different accounting treatments for intangible assets, was illustrated to support fudged accounting theory (Murphy, 1990). This paper extends that earlier work by examining the association between corporate leverage and accounting choice in the UK at a period when the extant accounting standard for goodwill, SSAP22 Accounting for Goodwill (ASC, 1989), permitted two very different accounting treatments. As a result, other intangibles, particularly brands, could avoid the regulatory strictures. For the present study, a series of hypotheses relating to corporate leverage and capitalization of intangible assets were tested. The results of the present study support fudged accounting theory by providing evidence that there is a relationship between the widespread capitalization of goodwill/brands and the relationship with leverage. The results demonstrate that financial managers will tend to adopt accounting practices that result in stronger balance sheets.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".