Creative Accounting as an Apparatus for Reporting Profits in Agribusiness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it