On the relationship between age and intentional invalid voting in compulsory elections
Bibliographic record
Abstract
Intentional invalid voting – the deliberate act of incorrectly marking one's ballot – poses a significant problem in compulsory voting systems. Indeed, some suggest that these “wasted” ballots raise concerns with regards to electoral legitimacy and the utility of compulsory voting in maximizing voter turnout. Previous research argues that younger voters disproportionately engage in intentional informal voting when compared to older members of the electorate. Using original cross-sectional data from a large sample of voters in the Australian state of Victoria (N = 25,246), we first show that there is only a small relationship between age and intentional informal voting. Building on existing theorizing, we then demonstrate that the relationship between age and informal voting is fully mediated by political disaffection, as measured by voters’ interest in politics, their satisfaction with democracy, and their satisfaction with candidate choices. We conclude with a discussion of the theoretical and policy implications of these results.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".