Who Feels They Can Understand and Have an Impact on Political Processes? Socio-demographic Correlates of Political Efficacy in 46 Countries, 1996–2016
Bibliographic record
Abstract
Abstract While recent research has produced robust objective evidence of unequal representation in democracies, there is little evidence about whether this inequality is consistent with individuals’ subjective perceptions of their own political efficacy. To answer this question, we use all available data on political efficacy from the International Social Survey Programme modules for 46 countries (1996–2016) to investigate trends and correlates of external and internal political efficacy. We focus on socio-demographic characteristics that are central to recent literature on unequal representation: gender, education, and income. Our individual-level findings show that education and income are positively associated with both external and internal efficacy while being female is associated with lower levels of internal efficacy but unrelated to external efficacy. We complement these individual-level analyses with a contextual investigation of how descriptive representation contributes to efficacy gaps. Focusing on gender, we show that women feel that they have more of a say in governmental decisions in contexts with a higher level of female representation among elected representatives. We conclude by noting how future research can leverage cross-national data to identify contextual mechanisms that may have an impact upon persistent social gaps in political efficacy across contexts and over time.
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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 | 0.003 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".