Combatting Corruption and Collusion in Public Procurement
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it