Age, Political Participation, and Political Context in Africa
Bibliographic record
Abstract
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".