REMOTE INTERNET VOTING FROM THE ANGLE OF PUBLIC TRUST: The cases of Switzerland, Canada, and Estonia
Bibliographic record
Abstract
The significance of trust to the adoption of electronic voting has been indicated by previous research. Recent evidence suggests that citizens’ trust is of great significance to remote Internet voting’s adoption. Factors found to be influencing the adoption of remote Internet voting are linked implicitly or explicitly to public trust. The research problem of this study is centered on the effect of trust on Internet voting’s adoption. In particular, this study aims at analysing the determinants that make some voters repose trust in Internet voting. The main theoretical focus is to deepen the understanding of the concept of trust and its role in Internet voting’s adoption. This study adopts a case study approach where the contexts of Switzerland, Canada and Estonia are evaluated comparatively. This study is also based on official reports and published survey results. Public trust is largely dependent on contextual factors, such as the enactment of specific legislation, high level of Internet penetration, inauguration of a publicity campaign aiming at informing citizens regarding remote Internet voting as well as trials and technological tests prior to online voting’s implementation. Public trust is also influenced by behavioural factors, such as confidence in new technologies, computing literacy, high frequency of Internet use and individual’s perception regarding his IT skills. The significance of the impact of voter’s perception regarding remote Internet voting’s security on the development of trust emerges also from the findings of this study. It can be concluded that the successful implementation of remote Internet voting does not lie in its perfect system but in electorate’s trust to the contrary. In particular, a high level of trust in the internet voting procedure signifies actual and constant use of online voting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".