Elder, Am I Right? Age-Group Differences in Social and Political Interactions in Africa
Bibliographic record
Abstract
Abstract Age-group differences play an essential role in social interaction across sub-Saharan Africa. However, the social effects of these differences remain understudied. We hypothesize that age-group differences will affect response patterns and use Afrobarometer data to test this hypothesis. We also explore three mechanisms through which age-group differences may induce response-pattern variation: in-group loyalty, social acquiescence, and social distance. As hypothesized, we find relatively large and statistically significant effects for age-group differences across a variety of questions. Our findings support in-group loyalty and social acquiescence rather than social distance when questions do not address age-related issues directly. However, social distance may play a more important role when questions address age-specific issues. Additionally, we show preliminary evidence that age-group differences induce larger response-pattern variation than coethnicity. Our findings speak to the importance of age in social interaction in Africa and provide important lessons for the survey research community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".