Legal Consequences of Mock Transactions
Bibliographic record
Abstract
In order to increase the material benefits, in order not to pay taxes or to pay less, in order to conceal information and for other purposes, the parties entering into legal relations become participants in mock transactions. The practise of mock transactions is to replace the conclusion of a single document, such as a sale one, with the conclusion of a contract of charitable contribution. The practise of using mock transactions is quite common and it is almost impossible to prove the nature of the transaction. Therefore, this work is aimed at investigating the institution of the mock transaction, as well as to develop recommendations for the practical application of the rules governing this institution. To conduct this study, the materials of the practise of dispute resolution on the application of the consequences of fictitious transactions by the courts of Ukraine, the dialectical method of cognition, the formal-legal method, the hermeneutic-legal method were used. As a result of research the signs of mock transactions, approaches of detection of fictitious transactions are established. It can be concluded that the distinguishing feature of fictitious and mock transactions is the orientation of the will of the parties to the transaction on the occurrence of legal consequences.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".