BidChain: A Blockchain-Based Decentralized Application for Transparent and Secure Competitive Tendering in Public Construction Projects
Bibliographic record
Abstract
Deficiencies of tendering systems in public projects can leave governments with subsequent issues in projects and waste taxpayers’ money. Electronic tendering (e-tendering) systems have been found beneficial for awarding public projects; however, current practices still have deficiencies. Legal and security issues and the lack of transparency have been identified as the main shortcomings in current tendering practices. This paper argues that the underlying issue with e-tendering systems is due to their centralized nature. Therefore, this research presents a critical assessment of the blockchain technology in developing distributed e-tendering systems and proposes a novel framework for decentralizing e-tendering systems. The proposed framework is created by integrating three technologies: blockchain, public key infrastructure (PKI), and interplanetary file system (IPFS). Using the proposed framework, a decentralized application (DApp) is developed to demonstrate the concept and evaluate the potentials of blockchain in mitigating the security and legal challenges and improving transparency and trust. The result shows that the reliance on centralized authorities for managing the tendering process is reduced in a blockchain-based e-tendering system, and a transparent and tamper-proof record of the tendering history is distributed among parties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".