Investigating the Effects of Telephone and Internet Voting on Election Administration in Rural Ontario Municipalities
Bibliographic record
Abstract
Existing research that has been conducted on telephone and internet voting in Canada has focussed almost exclusively on large, urban municipalities. Yet in Ontario, local governments of all sizes have adopted these modes of voting. As a result, very little is known about the effects of telephone and internet voting in rural municipalities and this is problematic. This paper attempts to address this gap in the literature by examining telephone and internet voting in the context of rural municipalities in Ontario. It relies on a qualitative multi-case cross-sectional design to generate exploratory findings on the effects that the adoption of telephone and internet voting has on election administration in rural Ontario municipalities. The data are drawn from semi-structured interviews held with key election administration staff within two rural municipalities in Ontario and are compared using interpretive methods. Analysis of the data suggests that there are nine different themes present in the experiences of rural municipalities. Each of these themes is treated as a different effect of adopting telephone and internet voting, and overall the findings suggest that the adoption of telephone and internet voting has tended to be a positive experience for election administrators in rural municipalities in Ontario.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".