Reinforcement Effects between Digital Media Use and Political Participation: A Meta-Analysis of Repeated-Wave Panel Data
Bibliographic record
Abstract
Abstract As digital media use has rapidly increased in prevalence and diversified in form, scholars across the globe have focused extensive attention on how the use of digital media relates to political participation. To assess the results of this emerging body of research, we conduct the first meta-analysis of repeated-wave panel data studies on the relationship between digital media use and political participation. The findings, based on 38 survey-based, repeated-wave panel studies (279 coefficients) bring new evidence to bear on two questions central to this literature. First, the findings provide new insight into the classic mobilization versus reinforcement debate: contrary to common assumption, the findings support a reinforcement effect, whereby those who are already politically active are motivated to use digital media. Second, the results indicate that the relationship between digital media use and political participation is durable, as studies with a longer time lag were more likely to yield positive and significant effects. Taken together, this evidence in support of a durable reinforcement effect implies the potential for digital media use to contribute to increased inequality in political participation over time. In the concluding discussion, we outline directions for further theoretical inquiry and empirical research that leverage the value of repeated-wave panel studies to make causal inferences.
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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 | 0.058 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".