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Record W2943831742 · doi:10.1177/0263395719844700

The influence of various measures of health on different types of political participation

2019· article· en· W2943831742 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolitics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPoliticsVotingVoting behaviorEuropean Social SurveyPolitical scienceSocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.389
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it