The Impact of Mobilization Media on Off-Line and Online Participation: Are Mobilization Effects Medium-Specific?
Bibliographic record
Abstract
In recent years, voluntary associations and political organizations have increasingly switched to Internet-based mobilization campaigns, replacing traditional forms of face-to-face recruitment and mobilization. The existing body of empirical research on Internet-based mobilization, however, is not conclusive about the effects this form of mobilization might have. In this article, the authors argue that this lack of strong conclusions might be due to the failure to distinguish different behavioral outcomes of mobilization, and more specifically, a distinction between online and off-line forms of participation is missing. In this experimental study, participants were exposed to potentially mobilizing information either by way of face-to-face interaction or by website. The results of the experiment indicate that web-based mobilization only has a significant effect on online participation, whereas face-to-face mobilization has a significant impact on off-line behavior, which would imply that mobilization effects are medium-specific. The authors close with some observations on what these findings might imply for the democratic consequences of the current trend toward an increasing reliance on Internet-based forms of political mobilization.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".