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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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