The “Quantitative Discretion Index”: A New Business Ethics Tool to Prevent Opportunistic Earnings Management Practices
Bibliographic record
Abstract
In this article, we build a Quantitative Discretion Index (hereafter referred to as QDI) to identify within the financial statements the most vulnerable areas related to possible opportunistic earnings management (hereafter referred to as EM) practices, with the aim of supporting ethical behaviour in corporate social communications. In order to better explain the QDI construction method, a practical example is implemented, starting from an analysis of the consolidated balance sheet of an Italian listed company operating in the media sector (in 2016). The QDI might be added to the contents of voluntary information provided by companies that pay attention to ethical behaviour and corporate social responsibility. Within each corporate balance sheet, the QDI allows stakeholders to identify the evaluation discretion areas, where any possible EM practices may be more likely and on which it may be more useful for stakeholders to focus their research attention. Business ethics aims to mitigate EM practices in social communications, including voluntary communication. Indeed, the discretional nature of the assessment of financial statements items by the administrative body represents one of the main weaknesses in the activity of mitigating earnings management practices. At present, the literature has dealt with the relations between ethical behaviour and EM; however, the research should also provide tools that can identify and neutralise the possibilities that opportunistic EM practices can be implemented, thus resulting in more ethical business practices.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".