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.
 
 
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".