Reported Corruption vs. Experience of Corruption in Public Procurement Contracts
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
This paper examines the accuracy of estimates of corruption reported in business surveys by comparing reported experience of corruption in public procurement from Malagasy firms having won public contracts with a more objective measure of corruption in the sector using a red flag indicator of corruption risk. This red flag indicator of corruption identifies contracts that failed to comply (or circumvented) public procurement regulations. We find that about 68 percent of public contracts in Madagascar in 2013 and 2014 were awarded with a method not complying with the Public Procurement Code and classified according to our methodology at risk of corruption, with 85% of contracting firms having won at least one corruption-prone contract. Matching public procurement data in Madagascar with a firm-level survey in 2015 among firms awarded public contracts in 2013-2014, we find that experience of corruption has no influence on firms' survey participation or propensity to answer questions about corruption.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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