The differentiated effects of health on political participation
Bibliographic record
Abstract
Background: Several studies have shown that an individual's state of health can significantly affect their decision to vote, but analysis is often only done on a single level of government; the national level. However, prior research has shown that the determining factors for voting can differ according to the level of government being considered. Our analysis is the first attempt to take a comprehensive look at the magnitude of health and political participation in a same country on different levels. Methods: Based on Canadian General Social Survey-Social Identity (2013; N = 27 695), we examined both the direct and indirect effect of self-rated health and self-rated mental health on (1) national voter turnout; (2) local voter turnout and (3) other forms of political participation. Results: The results show that health has a different effect on turnout depending the level of government. While health certainly affects participation on both levels of government, general health significantly affects national electoral participation levels while mental health more significantly affects electoral participation on the municipal level. Additionally, people who consider their mental health to be poorer, are more likely to sign an online petition. Conclusions: These elements highlight the necessity of questioning the cost of voting according to the level of government, and that further research into the potential offered by Internet and remote voting, is worthwhile-despite the opinions of critics who eschew these means of voting.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".