E-voting in Canada: Does age affect attitudes towards online voting?
Bibliographic record
Abstract
Elections need to remain innovative and robust in the fast-changing world in the digital age. While previous literature focuses on the technicality, legality, security and practicality of e-voting in Canada, the purpose of this study is to find if younger voters would adopt a different attitude towards online voting than older voters. The question at hand here is; how does age affect attitudes towards online voting in Canada? For populations that are under-represented in Canada, online voting can be a conduit that leads to better civic engagement, increased political participation and a better perception of elections. Online voting makes it easier for younger voters to engage in civic duties rather than going into polling stations and filling out a ballot in the electoral process. Often, a cost and benefit analysis is the mechanism behind rational choice voting. Therefore, there is reason to believe online voting may lead to a better sense of engagement for younger voters who are currently disengaged from our political system. Online voting could also lead to better representation for the interests of younger cohorts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".