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Outsourcing Repression

2022· book· en· W4283321868 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

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGrassrootsGovernment (linguistics)State (computer science)ChinaPower (physics)Resistance (ecology)Political scienceOutsourcingPolitical economySociologyLawPolitics

Abstract

fetched live from OpenAlex

Abstract How do states coerce citizens into compliance and minimize backlash at the same time? Outsourcing Repression portrays state engagement of nonstate actors—violent street gangsters and nonviolent grassroots brokers—to coerce and mobilize the masses for state pursuits in a manner that reduces resistance. This book draws on more than 200 interviews from ethnographic research conducted annually over a decade (2011‒2019) from the era of Hu Jintao to Xi Jinping, a unique and original event dataset, and a collection of government regulations to study everyday land grabs and housing demolition in China. Outsourcing Repression theorizes a counterintuitive form of state repression that reduces resistance and backlash. Everyday state power is quotidian power acquired through society by penetrating nonstate territories and mobilizing the masses within. This book uses China’s urbanization scheme as a window of observation and explains how the arguments can be generalized to other country contexts.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.099
Threshold uncertainty score0.998

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.027
GPT teacher head0.297
Teacher spread0.270 · 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

Quick stats

Citations54
Published2022
Admission routes1
Has abstractyes

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