Combatting Corruption and Collusion in Public Procurement:A Challenge for Governments Worldwide
Bibliographic record
Abstract
'Corruption' in public procurement typically involves procurement decisions taken in favour of preferred bidders in exchange for improper compensation (the acceptance of bribes, for example), while supplier collusion refers to a type of cartel activity, in which firms rig their bids in a tendering process. Although these practices are distinct, they frequently occur together in the public procurement context, reinforcing one another. Combatting Corruption and Collusion in Public Procurement: A Challenge for Governments Worldwide examines the causes of corruption and collusion in the public procurement sphere, its resulting harm, and how states can best try to combat these practices. This book provides a legal, economic, and practical analysis of issues concerning corruption and supplier collusion in public procurement, both generally and in seven diverse and representative jurisdictions: the United Kingdom, the United States, Brazil, Hungary and Poland, Ukraine, and Canada. It encompasses a discussion of both 'generic' cross-jurisdictional issues and specific proposals for individual jurisdictions. It offers practical guidance on building robust regimes for combatting corruption and collusion in public procurement and how to bolster and improve them when they are faltering. The book stresses the need for a multi-faceted and joined-up approach to the problems, emphasizing the importance both of enhanced investment in the effective enforcement of anti-corruption and cartel laws and of increasing the resilience of public procurement systems to corruption and collusion through a range of measures. The relevance of the topic to the social and economic well-being of citizens and the survival of democratic governance is highlighted throughout the book. Pioneering and comprehensive, Combatting Corruption and Collusion in Public Procurement provides a pathbreaking analysis of a range of global issues, making it an essential read for scholars, lawyers, government officials and representatives of international and non-governmental organizations around the world.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".