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Record W4392925522 · doi:10.1093/geronb/gbae035

Age, Political Participation, and Political Context in Africa

2024· article· en· W4392925522 on OpenAlexaff
Eugene Emeka Dim, Markus H. Schafer

Bibliographic record

VenueThe Journals of Gerontology Series B · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsContext (archaeology)DemocracyPolitical freedomMainstreamPolitical sciencePolitical economyDevelopment economicsSociologyGeographyLawEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: Political participation differs across the age range, but little is known about these patterns outside of developed countries. Political context is a particularly important consideration for all political behavior in Africa, where only a few countries are fully democratic. Drawing from political opportunity structures theory, we investigate how political freedom conditions the age-based pattern of electoral and nonelectoral political engagement, as well as protesting. METHODS: This study merges the fifth, sixth, and seventh rounds of the Afrobarometer data sets, spanning 36 African countries, with country-level data on political freedom from Freedom House. Using multilevel regression models, we examine how political freedom shapes the relationship between age and 3 forms of political participation. RESULTS: Africans aged from 18 to 60 years and living in nonfree countries are most engaged in electoral and nonelectoral political activities, though participation begins to drop markedly past age 60. For protest participation, young Africans living in partially and non-free countries are the most engaged in protests; yet limited political freedom again means a sharp age-based decline. DISCUSSION: The impact of political context on the age-participation association is nuanced in ways not anticipated by mainstream research on the developed West. Repressive regimes, while spurring engagement at younger ages, appear to disproportionately deter older Africans from political engagement, especially its riskiest forms. We conclude by calling for more country-comparative gerontological research with careful attention to contextual heterogeneity, particularly in the understudied Global South.

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.

How this classification was reachedexpand

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.647

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 studies0.0000.002
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 head0.117
GPT teacher head0.407
Teacher spread0.290 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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