E-Voting: A Novel of Generic Conceptual Framework
Bibliographic record
Abstract
The aim of this research is to analyze and identify factors that influence the success of e-Voting, develop an e-Voting framework, and to examine the proposed e-Voting framework for utilization based on countries, organizations, or institutions that will be introduce e-Voting for elections.Based on research conducted, the analysis and identification results show that there are three aspects that influence the success of e-Voting: readiness, public perception, and technology.The quantitative research was conducted, and the number of respondents were 403 that covered West Java, Indonesia.Based on results, is shows that the factors that influence the success of e-Voting are technology readiness, human resources readiness, trust in the technology, trust in the government, trust in the election commission, constitution readiness, and technological.Whereas the proposed generic e-Voting framework were consisting of the technology readiness index, human resources readiness, trust in the technology, trust in the government, and trust in the election commission, issue laws and policies, e-Voting technology development by conducting socio-technical research, e-Voting technology design and development, and technology acceptance model research that need to be assess.Hopefully, the proposed e-Voting framework will contribute to the new era of digital technology, especially to overcome the issues of traditional vote.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".