How Political Efficacy Relates to Online and Offline Political Participation: A Multilevel Meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
The rapid rise of digital media use for political participation has coincided with an increase in concerns about citizens' sense of their capacity to impact political processes. These dual trends raise the important question of how people's online political participation is connected to perceptions of their own capacity to participate in and influence politics. The current study overcomes the limitation of scarce high-quality cross-national and over-time data on these topics by conducting a meta-analysis of all extant studies that analyze how political efficacy relates to both online and offline political participation using data sources in which all variables were measured simultaneously. We identified and coded 48 relevant studies (with 184 effects) representing 51,860 respondents from 28 countries based on surveys conducted between 2000 and 2016. We conducted a multilevel random effects meta-analysis to test the main hypothesis of whether political efficacy has a weaker relationship with online political participation than offline political participation. The findings show positive relationships between efficacy and both forms of participation, with no distinction in the magnitude of the two associations. In addition, we tested hypotheses about the expected variation across time and democratic contexts, and the results suggest contextual variation for offline participation but cross-national stability for online participation. The findings provide the most comprehensive evidence to date that online participation is as highly associated with political efficacy as offline participation, and that the strength of this association for online political participation is stable over time and across diverse country contexts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it