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Record W3010277783 · doi:10.1177/0192512120905048

Political consumerism: A meta-analysis

2020· article· en· W3010277783 on OpenAlexaff
Lauren Copeland, Shelley Boulianne

Bibliographic record

VenueInternational Political Science Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMacEwan University
FundersBaldwin Wallace University
KeywordsConsumerismDistrustPoliticsIdeologyVoting behaviorPolitical communicationPolitical scienceSociologyPolitical economyPublic relationsVotingLaw

Abstract

fetched live from OpenAlex

Political consumerism refers to the deliberate purchase or avoidance of products, goods, or services for political reasons. For decades, researchers have studied the micro-level predictors of political consumerism in many countries and across a variety of contexts. However, many questions remain. Do resource-based models of political participation or theories of lifestyle politics best explain why some people are more likely to engage in political consumerism? To answer this question, we conduct a meta-analysis of 66 studies with more than 1000 tests. We find more support for theories of lifestyle politics. Political consumerism is associated with political distrust, liberal ideology, and media use, as well as education, political interest, and organizational membership. The findings help us understand the subset of people who are using their purchasing power to express political opinions. They also help us identify gaps in existing research.

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)0.025
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.031
Bibliometrics0.0070.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.233
GPT teacher head0.478
Teacher spread0.245 · 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 designMeta-analysis
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

Citations160
Published2020
Admission routes1
Has abstractyes

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