Redefining Governmental Services Through Blockchain and Smart Contracts
Bibliographic record
Abstract
This study explores the potential of blockchain technology to redefine public administration, focusing on the integration of Ethereum, a blockchain platform, and the Interplanetary File System (IPFS) for notarial certification issuance.The core aim is to evaluate the capacity of this technology to augment governmental efficacy, ensure transparency in service provision, decentralize data management, and maintain information integrity.The architectural components of the system comprise Ethereum's smart contracts, Ether, gas, and a decentralized application, supplemented by IPFS as a decentralized file storage system for a secure and transparent certificate issuance mechanism.Scalability assessments indicated efficient processing of multiple transactions per second (TPS), suggesting the system's capability to service a considerable number of simultaneous users.The encryption and decryption performance exhibited by IPFS, particularly for small content sizes of 250 KB and 500 KB, was near-instantaneous.Average times for deployment and execution were recorded as 9 seconds and 6 seconds, respectively.In conclusion, the synergistic integration of Ethereum and IPFS exhibits the potential to significantly transform public administration by augmenting efficiency and transparency in the delivery of citizencentric services.Notably, the incorporation of IPFS for secure file storage and hash transmissions was instrumental in optimizing cost-efficiency.
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".