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Record W4376106488 · doi:10.1177/01925121231163548

Healthy citizens, healthy democracies? A review of the literature

2023· review· en· W4376106488 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

VenueInternational Political Science Review · 2023
Typereview
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
FundersKulttuurin ja Yhteiskunnan Tutkimuksen ToimikuntaAcademy of Finland
KeywordsPoliticsIncentiveHealth equityCausality (physics)Political scienceInequalityDemocracySociologyDevelopment economicsPolitical economyPublic relationsHealth careEconomics

Abstract

fetched live from OpenAlex

A growing literature over the past 10 years on health and political behavior has established health status as an important source of political inequality. Poor health reduces psychological engagement with politics and discourages political activity. This lowers incentives for governments to respond to the needs of those experiencing ill health and thereby perpetuates health disparities. In this review article, we provide a critical synthesis of the state of knowledge on the links between different aspects of health and political behavior. We also discuss the challenges confronting this research agenda, particularly with respect to measurement, theory, and establishing causality, along with suggestions for advancing the field. With the COVID-19 pandemic casting health disparities into sharp focus, understanding the sources of health biases in the political process, as well as their implications, is an important task that can bring us closer to the ideals of inclusive democracy.

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)0.009
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.555
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.004
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.106
GPT teacher head0.507
Teacher spread0.401 · 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