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Record W2334726107 · doi:10.1177/0010414015626437

Disguised Collective Action in China

2016· article· en· W2334726107 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

VenueComparative Political Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCollective actionBureaucracyMandateAuthoritarianismCivil societyPolitical sciencePublic relationsChinaState (computer science)Action (physics)CoachingEthnographySociologyLawManagementDemocracyEconomics

Abstract

fetched live from OpenAlex

How does civil society mobilize citizens in an authoritarian state that forbids organizations from coordinating collective contention? Drawing on ethnographic fieldwork in underground labor organizations in China, this article theorizes a tactical innovation—disguised collective action—that lowers the cost of organizing contention under repression. Instead of forming organizations to facilitate collective action, organizations enable citizens to better contend as individuals. Departing from processes captured by the “dynamics of contention” framework, organizations act as unconventional mobilizing structures by coaching aggrieved citizens to make individual rights claims without engaging in perilous collective protests. Through a hidden pedagogical process, claimants are coached to deploy a repertoire of atomized actions that targets the bureaucratic mandate to maintain social stability and also appeals to officials’ moral authority. When effective, disguised collective action can secure concessions for participants while allowing activists to strike a middle ground between challenging authorities and organizational survival.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.444

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 studies0.0000.001
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 head0.170
GPT teacher head0.449
Teacher spread0.279 · 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