Public Procurement: Current Challenges, Trends, and Management Efficiency
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
The article explores current challenges, trends, and directions for improving the efficiency of public procurement in Ukraine under martial law and economic turbulence. Particular attention is paid to the role of public procurement as a tool for economic stabilization, anti-corruption, and competition stimulation. The purpose of the article is to analyse the current state of the public procurement system, identify problems in its functioning, and justify managerial decisions for its improvement. The methodological framework includes a systematic approach, methods of economic and comparative analysis, SWOT analysis, case studies, cost-effectiveness analysis, and a review of international procurement systems (EU, USA, Canada, UK). The study identifies key issues in Ukraine’s public procurement system: low competition levels, insufficient anti-corruption regulation, and limited transparency in contract execution. The effectiveness of the Prozorro digital platform is assessed, and the potential of artificial intelligence, blockchain, and Big Data technologies in procurement is highlighted. The article proposes directions for system improvement, including harmonization of legislation with EU requirements, enhanced monitoring, institutional oversight, and the implementation of innovative management tools.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it