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Record W4221137707 · doi:10.1177/14614448221083025

Digital media and political consumerism in the United States, United Kingdom, and France

2022· article· en· W4221137707 on OpenAlexafffund
Shelley Boulianne, Lauren Copeland, Karolina Koç-Michalska

Bibliographic record

VenueNew Media & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMacEwan University
FundersSocial Sciences and Humanities Research Council of CanadaBaldwin Wallace University
KeywordsPoliticsConsumerismSocial mediaPolitical communicationPolitical scienceSociologySalientPolitical economyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.038
GPT teacher head0.307
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2022
Admission routes2
Has abstractyes

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