The last-minute voter: age and time of voting in Ontario, Canada
Bibliographic record
Abstract
This article contributes to the literature on the “costs of voting” by examining when an individual decides to vote, in terms of both the date they cast their ballot and the time of day they may visit a polling station. There may be important variations in voters’ choices of when to vote, with implications for a variety of lines of research, including for the efficacy of convenience voting mechanisms to truly make voting easier for those least likely to already vote; the impact on a voter’s experience of the election; the consequences for election campaigns and the deliberative environment; and the impacts on election administration and planning. This article contributes to this research using a new dataset of individual-level voter information during the 2022 Ontario provincial general election, using original voter-level participation data, provided to the researchers by Elections Ontario (the provincial electoral management body), combined with census data at the dissemination area level. This article demonstrates how certain socio-demographic and geographic characteristics, notably a voter’s age, relates to choices of when a voter will cast their ballot. This work broadens our understanding of the experience of electors in this sub-national context and the decision of when to cast a ballot.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".