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Record W2794090321 · doi:10.1177/1043463117754078

The role of rationality in motivating participation in social movements: The case of anti-Japanese demonstrations in China

2018· article· en· W2794090321 on OpenAlexaff
Min Zhou, Hanning Wang

Bibliographic record

VenueRationality and Society · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInterpersonal communicationRationalitySocial psychologyMediationPsychologyAffect (linguistics)Interpersonal tiesBeijingChinaConstruct (python library)PerceptionIdentity (music)Collective identitySociologyPolitical science

Abstract

fetched live from OpenAlex

This study proposes a theoretical model that integrates the rational approach with the structural and cultural approaches to explain motivations for participation in social movements. In this integrative model, rational perceptions about the benefits and costs of participation have both mediation and interaction relations with structural and cultural motivators. First, rational perceptions mediate the motivating effects of interpersonal ties to prior participants and collective identity. Interpersonal ties and collective identity construct individuals’ perceived benefits and costs, which in turn affect their participation motivations. Second, perceived benefits and costs also interact with interpersonal ties and collective identity in affecting participation motivations. Interpersonal ties and collective identity may affect how sensitive the individual is to the formed rational perceptions. Especially, interpersonal ties weaken the motivating effect of perceived benefits. We apply this model to the case of China’s recent nationwide anti-Japanese demonstrations. Using original data from a large-scale survey on 1458 university students in Beijing, we find this integrative model effective in explaining university students’ motivations to participate in future anti-Japanese demonstrations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.502
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.375
Teacher spread0.344 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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