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Record W4213421796 · doi:10.1002/ejsp.2847

Do the means affect the ends? Radical tactics influence motivation and action tendencies via the perceived legitimacy and efficacy of those actions

2022· article· en· W4213421796 on OpenAlexaff
Morgana Lizzio‐Wilson, Emma F. Thomas, Winnifred R. Louis, Catherine E. Amiot, Simon M. Bury, Pascal Molenberghs, Jean Decety, Monique F. Crane

Bibliographic record

VenueEuropean Journal of Social Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsInjusticeLegitimacyPsychologyCollective actionSocial psychologyAffect (linguistics)Action (physics)Collective identityIdentity (music)Collective efficacyPolitical scienceCommunication

Abstract

fetched live from OpenAlex

Abstract Identity, injustice and group efficacy are key motivations for collective action engagement. However, little work has examined factors that influence their emergence. Across three studies (total N = 938), we test whether exposure to different actions (i.e., radical or conventional) and the perceived legitimacy and efficacy of those actions (‘the means’) predict observers’ sense of injustice, identity, group efficacy about the issue, and in turn, future action engagement (‘the ends’). As expected, radical (versus conventional) actions were perceived as less legitimate and effective. These evaluations indirectly predicted lower action via diminished identification and injustice, respectively. Paradoxically, legitimacy and efficacy evaluations also indirectly predicted higher radical and conventional action via diminished group efficacy. Thus, collective action is shaped by and reciprocally influences injustice, identity, and group efficacy. Simultaneous exposure to conventional and radical actions also offset these effects, indicating that conventional actions can mitigate the indirect effects of radical tactics.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.397
Teacher spread0.305 · 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 teacher head, not a consensus.

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

Citations22
Published2022
Admission routes1
Has abstractyes

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