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Record W2811376662 · doi:10.1163/15685268-00192p04

Heroes, Hooligans, and Knights-Errant: Masculinities and Popular Media in the Early People’s Republic of China

2017· article· en· W2811376662 on OpenAlexaff
Y. Yvon Wang

Bibliographic record

VenueNAN Nü · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRhetoricChinaCommunismMasculinitySubjectivityTrope (literature)State (computer science)SociologyPopularityLawGender studiesHistoryPolitical sciencePolitical economyLiteratureArtPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This article is an exploration of media and gender in urban and peri-urban China during the 1950s and early 1960s – specifically, the persistent trope of the “hooligan,” or liumang . Since at least the late imperial period, Chinese authorities had feared unmarried, impoverished, rootless men as the main source of crime, disorder, and outright rebellion. Yet such figures were simultaneously celebrated as knights-errant for their violent heroism in cultural works of enormous popularity across regions and classes. As the ruling Chinese Communist Party attempted to reshape society and culture after 1949, it condemned knight-errant tales and made hooliganism a crime. At the same time, the state tried to promote a new pantheon of vigilante-like men in the guise of revolutionary heroes. But the state’s control over deeply rooted cultural markets and their products was incomplete. Moreover, the same potent tools that had empowered the Party, in particular its rhetoric of revolutionary subjectivity and its harnessing of modern media technologies, were open as never before to being adopted by the very targets of its efforts at control and censure. Marginal masculinity in the early PRC, though in many ways continuous with that in China during the previous decades and centuries, marked a new epoch: men and boys deemed hooligans were able to speak out and defend themselves as heroes.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.276
Teacher spread0.246 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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