Powerless in the digital age? A systematic review and meta-analysis of political efficacy and digital media use
Bibliographic record
Abstract
Many citizens feel powerless in the current globalized political context, despite the potential of digital media to increase their perceptions of being informed about politics and expand their opportunities to interact with elected officials to try to influence government decisions. We analyzed 193 studies to document the most popular ways to conceptualize, measure, and model political efficacy when also studying digital media. Furthermore, we conducted a meta-analysis of correlations. We find that the positive estimates are larger, on average, when considering internal political efficacy and smaller but still positive when considering external political efficacy. We also examine how the relationships differ according to the type of media use and political system, whether authoritarian (e.g. China) or democratic. We propose a theoretical framework that considers reciprocal effects. Online information may contribute to feelings of being informed about politics and feelings of being informed lead to online political participation.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".