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Record W2795249882 · doi:10.1177/0032321718763558

What Drives People to Protest in an Authoritarian Country? Resources and Rewards vs Risks of Protests in Urban and Rural China

2018· article· en· W2795249882 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolitical Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAuthoritarianismChinaGovernment (linguistics)PoliticsAccountabilityPolitical scienceState (computer science)Survey data collectionPolitical economyDevelopment economicsSociologyEconomicsDemocracyLaw

Abstract

fetched live from OpenAlex

What drives people to protest in an authoritarian country? Drawing from a rich set of individual-level data from the China General Social Survey 2010, we address the question of protest participation by focusing on the factors of resources, and rewards vs risks, that might be unique to protestors in an authoritarian state. We find strong evidence for education, typically conceived as a key enabling resource in protests, to be negatively associated with likelihood of participation. There are, however, significant differences between political behavior in urban and rural samples. We find some, though rather weak, evidence to suggest that as urban residents become wealthier over time, they will increasingly turn to protests as a form of political participation, demanding greater accountability of government and corporate actions.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.220
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.377
Teacher spread0.340 · 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