Risks of Small and Medium-Sized Businesses Attracting to Government and Municipal Procurement and Economic Growth
Bibliographic record
Abstract
One of the strategic goals in the Russian Federation is to relieve social tension by increasing the number of people employed in the segment of small and medium enterprises (hereinafter - SMEs) up to 25 million people by 2024. In order to stimulate the development of this segment, the Government of the Russian Federation sets the task of expanding the access for SMEs to the procurement system. That’s why the analysis of the existing mechanism for attracting SMEs to procurement is of interest. The primary purpose of the study is to investigate the peculiarities of SME involvement in procurement within the framework of two main laws operating in the sphere of procurement activity and problems arising in the procurement’s organization and economic growth. The practice of procurement activity is accompanied by the emergence of risks that affect the procurement result. These risks are inherent both to customers and suppliers of procurement activities. In the article risks are classified by stages of procurement activity. In particular, risks of poor-quality performance of the contract arise long before the stage of its conclusion. In the conclusion, recommendations are made which can be directed to solving the issues of SMEs access to procurement.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".