The influence of various measures of health on different types of political participation
Bibliographic record
Abstract
Recent research in political behaviour suggests that poor health can be an impediment for individuals to vote. At the same time, researchers argue that health may both hinder and reinforce other forms of political participation. With respect to these ambiguous expectations, our study asks: does the relationship between health and political involvement depend on how we measure health? We answer this question for two of the most widely used health indicators, self-reported health and being hampered by illness in daily activities. We use the European Social Survey (ESS) (N = 35,000) covering 20 European countries and find that the measurement of health indeed matters: our results illustrate that bad self-reported health is an impediment to voting, but not to other forms of political activity. When it comes to our second indicator, being hampered in daily activities, we also find a negative relationship with voting. Yet, our results also indicate that most individuals, who are hampered by illness in their daily lives, have a tendency to participate more regularly in most other forms of political activity, including boycotting, contacting a politician, or signing a petition. Robustness checks including waves 1–6 of the ESS support these findings.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".