Prototyping a Smart Contract Based Public Procurement to Fight Corruption
Bibliographic record
Abstract
Corruption in public procurement is a worldwide appearance that causes immense financial and reputational damages. Especially in developing countries, corruption is a widespread issue due to secrecy and lack of transparency. An important instrument for transparency and accountability assurance is the record which is managed and controlled by recordkeeping systems. Blockchain technology and more precisely blockchain-based smart contracts are emerging technological tools that can be used as recordkeeping systems and a tool to mitigate some of the fraud involving public procurement records. Immutability, transparency, distribution and automation are some of the features of smart contracts already implemented in several applications to avoid malicious human interference. In this paper, we discuss some of the frauds in public procurement, and we propose smart contracts to automatize different stages of the public procurement procedure attempting to fix their biggest current weaknesses. The processes we have focused on include the bidding process, supplier habilitation and delivery verification. In the three subprocesses, common irregularities include human fallibility, improper information disclosure and hidden agreements which concern not only governments but also civil society. To show the feasibility and usability of our proposal, we have implemented a prototype that demonstrates the process using sample data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".