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Record W4241285327 · doi:10.31235/osf.io/z3myc

Under What Conditions are Students Willing to Protest? Selective Incentives, Production Functions, and Grievances

2020· preprint· en· W4241285327 on OpenAlexfundno aff
Ross L. Matsueda, Blaine G. Robbins, Steven Pfaff

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentYork UniversityEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of WashingtonNational Science Foundation
KeywordsGrievanceIncentiveContext (archaeology)Production (economics)Social psychologyCollective actionPsychologyFunction (biology)Test (biology)Political scienceSociologyEconomicsMicroeconomicsLaw

Abstract

fetched live from OpenAlex

This article tests a theory of student protest based on collective action theories. Drawing on rational choice theories of selective incentives, critical mass theories of production functions, and social psychological theories of protest, the present article specifies a theory of willingness to protest. To test our model, we administer a factorial survey experiment of student protest to a random sample of undergraduate students. We find that both the perceived likelihood of a protest’s success and one’s intention to protest are affected by the magnitude of the grievance, selective rewards and punishments, and the number of participants. The latter effect suggests a decelerating production function. Finally, we find that the likelihood of success mediates much of the effect of social context on intention to protest, implying that actors consider the effects of incentives not only on their own behavior but also on the behavior of others.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.042
GPT teacher head0.353
Teacher spread0.311 · 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 designQualitative
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

Citations6
Published2020
Admission routes1
Has abstractyes

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