COMPARATIVE ANALYSIS OF FOREIGN PRACTICE OF JUSTIFICATION OF THE INITIAL MAXIMUM PRICE OF THE CONTRACT IN THE PUBLIC ORDER PLACEMENT SYSTEM
Bibliographic record
Abstract
The paper highlights the main aspects of the formation and justification of the maximum price when placing a state order by participants in the contract system of foreign countries. The analysis of the best practices in public procurement is described on the example of countries such as the USA, Canada, UK, France, Brazil, Australia. Differences in comparison with the domestic practice of justifying the initial maximum price are revealed, associated with the obligatory survey of participants in the procurement procedure by government customers, with explanations of the decision taken by the commission of the government customer. The work reflects foreign analogues of the concept of the initial maximum contract price used in Russian practice, which are subject to the principles of agreeing a reasonable and fair price, as well as the open and hidden nature of the reserve price. The essence of the concept of the preliminary price of a state contract mentioned in the directives and guidelines of the European Union is revealed. The article reveals the factors enshrined in foreign regulations that prevent the formation of an equilibrium market price in the system of placing a government order, associated not only with a conflict of interest, but also with the unjustified provision of cost and non-cost benefits
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".