How to Reduce Corruption in Public Procurement: The Fundamentals
Bibliographic record
Abstract
Procurement of goods, works and other services by public bodies alone amounts on average to between 15% and 30% of Gross Domestic Product (GDP), in some countries even more. Few activities create greater temptations or offer more opportunities for corruption than public sector procurement. Damage from corruption is estimated at normally between 10% and 25%, and in some cases as high as 40 to 50%, of the contract value.Public procurement procedures often are complex. Transparency of the processes is limited, and manipulation is hard to detect. Few people becoming aware of corruption complain publicly, since it is not their own, but government money, which is being wasted.This document is Part I of the Handbook for Curbing Corruption in Public Procurement published by Transparency International in 2006 and its purpose is to provide an overview of the problem of corruption in public contracting. Sections 2 and 3 of the Handbook, written by other authors, offer suggestions and experiences of how this problem can be addressed. The full text of the Handbook has been made available.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".