Digital media and political consumerism in the United States, United Kingdom, and France
Bibliographic record
Abstract
Digital media use can connect citizens across geographic boundaries into coordinated action by distributing political information, enabling the formation of groups, and facilitating political talk. These activities can lead to political consumerism, which is an important and popular form of political participation that translates across geographic borders. This article uses original survey data ( n = 9284) to examine the relationship between digital media use and political consumerism in the United States, United Kingdom, and France. Talking politics online, joining social groups on social media, and searching online for political information increase participation in political consumerism. However, the strength of these positive correlations differs by age, country, and mode of political consumerism. Joining social groups on social media has a much larger effect size on buycotting compared to boycotting. The findings imply that social groups are more salient in the mobilization process for buycotting campaigns compared to boycotting campaigns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".