Towards Citizen-Centric Services using Blockchain-Powered Digitalization of Public Sector Processes
Bibliographic record
Abstract
Public sector institutions operate in a rapidly changing landscape that is transforming the way services are delivered. To ensure positive outcomes for citizens, governments need the support of efficient public institutions with the capacity to keep track with emerging trends and changing expectations. The adoption of new technologies offers innovative opportunities for the public sector and can improve interactions between citizens and institutions and enabling citizen-centric services. The emergence of blockchain technology has inspired a new type of information exchange infrastructure on a peer-to-peer model that makes possible a set of use cases in sectors where improved efficiency, reliability, security and costs around transparency are intended. The public sector is mainly characterized by administrative processes that involve several institutions. If blockchain can promote various types of integration in an organization, the implications around its applicability and its interoperability in the complex integration of an inter-organizational public process remain to be demonstrated. In this paper, we investigate a real-life use case in public administration related to the vehicle registration process involving multiple institutions to develop an architecture that makes possible a blockchain-based digitalization of the inter-organizational process. We describe a case study and use it to extract the system requirements, analyze an approach that identifies coordination and data interoperability in public sector inter-organizational processes, and propose an architecture focused on transparency and privacy for effective deployment. We then implement the designed architecture around the scenarios of the vehicle registration.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".