Electronic voting research papers in web of science: A bibliometric analysis
Bibliographic record
Abstract
Purpose - The main aim of the paper is to investigate and recognize trends in Electronic Voting research at aninternational level Design/methodology/approach - Retrievalof the data was from the database of Web of Science on the topic Electronic Votingthat covered the period from 1988- 2020 to identify useful contributions that have been producedand published in the respective field A total of 994 data records were retrieved from the database Several trends predominatingin Digital Voting research including well known contributing countries, adopted patterns of the authorship, the degree of co-authorship, cross-country co-authorship, prominent sources for research publication, recognition of research in courseof citation trends like average citation per year, top-cited papers, citations received/citations per paper, etc were discovered by analysis of the data Findings–There have been constructive growth in the literature since 1988 as revealed by the analysis As evident from the analysis, half of the research output was contributed by the five countries – USA, England, China, Spain, and Germany with a total of 491 journals published When it comes to the effectiveness of the papers, Denmark leads with a PEI of 4 87 It is followed by Canada with PEI of 4 60 and Scotland with PEI of 2 80 Most of the journals belong to Computers Science and Political Science categories of Web of Science Also, the number of times these papers are cited per year is increasing rapidly for the given duration This shows there is an increase in research in the field of Digital Voting COVID-19 pandemic can be made accountable for the irregularity in the trend in 2020 that has halted the research in the field On performing linear regression, it was observed that we can expect positive growth in the number of citations in the coming years © 2020 Ubiquity Press All rights reserved
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.085 | 0.563 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".