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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 OpenAlexaff
Lynette H. Ong, Donglin Han

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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

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

Citations38
Published2018
Admission routes1
Has abstractyes

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