Not Unique, not Universal: Risk Perception and Acceptance of Online Voting Technology by Russian Citizens
Why this work is in the frame
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Bibliographic record
Abstract
What is the connection between Russian citizens' perception of Internet voting and the context of its top down adoption with their readiness to use it? To investigate this question, we use Structural Equation Modeling (SEM) to account for both observed and latent indicators of technology adoption and their linkage with the Internet voting use intent. The authors use survey evidence from VCIOM (2020) and a national survey of Internet users conducted by Online Marketing Intelligence (OMI) company in 2021. This study provides some support to the application of theoretical expectations formulated in the context of Western democracies to the Russian population's voting technology attitudes. The findings indicate that the use of the Internet is not a robust measure of technology acceptance anymore, and a more nuanced approach to the experiences of Internet usage is needed. Internet users appear to be more concerned about privacy, the possibility of fraud, and external interference than the respondents drawn from the overall population. The authors suggest that it is due to acceptance of risks seeming inevitable and to bigger digital literacy and therefore awareness about the risks posed by voting online. Acknowledgments. The authors are grateful to anonymous reviewers for their astute observations and criticism. For their helpful comments, we thank our senior colleagues at HSE University: A. S. Akhremenko, K. L. Marquardt, and M. G. Mironyuk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it