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Record W2783175379 · doi:10.1111/asap.12145

The State of American Protest: Shared Anger and Populism

2018· article· en· W2783175379 on OpenAlexaff
Amber M. Gaffney, Justin D. Hackett, David E. Rast, Zachary P. Hohman, Alexandria Jaurique

Bibliographic record

VenueAnalyses of Social Issues and Public Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAngerRelative deprivationFeelingPopulismSocial psychologyPsychologyCollective actionPoliticsCollective identityDemocracyPerceptionConventionPolitical scienceCriminologyLaw

Abstract

fetched live from OpenAlex

Abstract Social psychological models of group identity and collective action should be particularly adept at providing psychological explanations for growing rates of populism in the Western World. Because populism tends to arise in times of societal shifts that reflect both economic and cultural changes, populist attitudes are likely grounded in perceptions of intergroup relations and collective attitudes. We surveyed 95 demonstrators at the 2016 Republican National Convention and 108 demonstrators at the Democratic National Convention. Results support the idea that relative anger prototypicality, in this case, the extent to which people believe that their own anger toward politicians is representative of most Americans’ anger, predicts feelings of group‐based relative deprivation. Importantly, these feelings of deprivation mediate the relationship between prototypical anger and populist attitudes. These findings provide a unique picture of current political engagement, motivated by feelings of shared anger and collective feelings of lacking representation and voice.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.420
Teacher spread0.364 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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