Creative Accounting as an Apparatus for Reporting Profits in Agribusiness
Bibliographic record
Abstract
The economic results of a company are an important tool for many entities, e.g., for internal entities as well as for external entities. As the economic results of a company are often the only source of information that informs the company’s partners about the managerial activities of their company, it is necessary to present these economic results using real numbers. However, companies prefer to achieve better results by applying the principles of creative accounting, which leads to improved economic values being shown to be achieved during an accounting period. The purpose of this article is to apply models that have been developed to detect creative accounting, which occurs under conditions that help enterprises to adjust their financial statements and tax bases and involves using creative accounting techniques to become competitive or to be able to take advantage of deductions. These models were applied to the Slovak Republic’s agriculture, forestry, and fishing sector (sector A), which is highly affected by earnings manipulation. This article provides a numerical expression of companies, which were previously, with some probability level, involved in conducting financial statement manipulation. Subsequently, the results that were obtained have been displayed using receiver operating characteristic (ROC) curves. The outputs of the analysis show that a large proportion of the companies in this sector tend to use creative accounting, which is not only harmful for entrepreneurs and their business partners in sector A, but also for the Slovak Republic at large, as the Slovak government cannot determine whether the reported accounting results reflect a company’s real financial situation.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".