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Record W2162730466 · doi:10.1002/ejsp.1983

The ironic impact of activists: Negative stereotypes reduce social change influence

2013· article· en· W2162730466 on OpenAlexafffund
Nadia Bashir, Penelope Lockwood, Alison L. Chasteen, Daniel Nadolny, Indra Noyes

Bibliographic record

VenueEuropean Journal of Social Psychology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of WaterlooUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSocial psychologyMilitantSocial changePsychologyPerceptionSocial activismResistance (ecology)Social learningSocial perceptionPoliticsPolitical science

Abstract

fetched live from OpenAlex

Abstract Despite recognizing the need for social change in areas such as social equality and environmental protection, individuals often avoid supporting such change. Researchers have previously attempted to understand this resistance to social change by examining individuals' perceptions of social issues and social change. We instead examined the possibility that individuals resist social change because they have negative stereotypes of activists, the agents of social change. Participants had negative stereotypes of activists (feminists and environmentalists), regardless of the domain of activism, viewing them as eccentric and militant. Furthermore, these stereotypes reduced participants' willingness to affiliate with ‘typical’ activists and, ultimately, to adopt the behaviours that these activists promoted. These results indicate that stereotypes and person perception processes more generally play a key role in creating resistance to social change. Copyright © 2013 John Wiley & Sons, Ltd.

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)0.010
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.065
GPT teacher head0.411
Teacher spread0.346 · 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

Citations230
Published2013
Admission routes2
Has abstractyes

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