Combatting Corruption and Collusion in Public Procurement
Bibliographic record
Abstract
Abstract This book considers why corruption and collusion continue to undermine public procurement processes despite national and international efforts to combat them. It also makes proposals for reforms aimed at combatting these practices and helping countries to defend the integrity of their public procurement systems. It examines why public procurement processes are especially prone to distortion by corruption and/or collusion, the harm these practices cause, the basic frameworks that countries adopt to limit the scope for corruption and supplier collusion in their public procurement systems, and how the effectiveness of these foundational frameworks can be optimized, strengthened, and bolstered to ensure that they achieve their objectives and are not prevented by weaknesses within them. Recognizing that, even if they may embody common elements, the challenges of implementing and embedding an effective system vary across jurisdictions; subsequent chapters go on to examine the particular contexts of, and make proposals for reform in, seven discrete jurisdictions, the United Kingdom, the United States, Brazil, Hungary, Poland, the Ukraine, and Canada. It concludes by drawing together the book’s overall findings and reform proposals and highlighting some core points relating to the general and jurisdiction-specific discussions. An overarching theme includes the real need in all states to recognize the pervasive nature, and high risk, of corruption and collusion impacting public procurement, and the necessity to hone and develop public procurement systems routinely to counter the compelling incentives for such conduct, to block opportunities for it, and to encourage compliance with relevant laws.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".