Criminal law protection of public procurement in the USA, Canada and the European Union
Bibliographic record
Abstract
Криминализация в Российской Федерации общественных отношений, связанных с посягательством на сферу публичных закупок, вызвала дискуссии по поводу эффективности предлагаемых законодателем механизмов уголовно-правовой охраны. Исследование зарубежного опыта уголовной ответственности за посягательство на сферу публичных закупок, особенно опыта тех стран, где данная сфера имеет продолжительную историю правового регулирования, представляет интерес с позиции определения эффективности ст. 200.4-200.6 УК РФ. The criminalization of violations in the field of public procurement in the Russian Federation has caused discussions about the effectiveness of the mechanisms of criminal law protection proposed by the legislator. The study of foreign experience in criminal liability for encroachment on the field of public procurement, especially the experience of those countries where this area has a long history of legal regulation, is of interest from the standpoint of determining the effectiveness of Art. 200.4-200.6 of the Criminal Code of the Russian Federation.
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How this classification was reachedexpand
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 | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".