Enhancing transparency and accountability in public procurement: exploring blockchain technology to mitigate records fraud
Bibliographic record
Abstract
Purpose Fraud within the procurement process remains a persistent challenge, resulting in substantial financial losses and lack of social justice. This paper underscores the significance of records for the integrity of the procurement practices and proposes using blockchain technology to mitigate records fraud. Analyzing international regulations this paper highlights their emphasis on proper records management for promoting transparency, accountability, and integrity of procurement procedures. This paper aims to contribute to a comprehensive understanding of the relationship between records management and procurement accountability while addressing blockchain technology's innovative use in mitigating records forgery and omission. Design/methodology/approach This research involves a comparative analysis of international regulations investigating their directives on the relevance of records in public procurement and a survey of records fraud cases in the Brazilian context to illustrate the significance of the problem and to indicate how blockchain technology can be applied as a solution to ensure accountability and prevent records forgery and omission. Findings The findings highlight the explicit importance ascribed to proper records management by international regulations, and indicates how blockchain technology can serve as a valuable resource to reduce the records fraud opportunity in public procurement. Research limitations/implications The research does not consider context-specific regulations. The survey of frauds is limited to the Brazilian context. Originality/value This research introduces a pioneering approach by investigating the use of blockchain technology to combat records forgery or omission in public procurement procedures.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".