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Record W4231071323 · doi:10.1525/9780520938991

Millennial Monsters

2019· book· en· W4231071323 on OpenAlexaboutno aff
Anne Allison

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

From sushi and karaoke to martial arts and technoware, the currency of made-in-Japan cultural goods has skyrocketed in the global marketplace during the past decade. The globalization of Japanese “cool” is led by youth products: video games, manga (comic books), anime (animation), and cute characters that have fostered kid crazes from Hong Kong to Canada. Examining the crossover traffic between Japan and the United States, Millennial Monsters explores the global popularity of Japanese youth goods today while it questions the make-up of the fantasies and the capitalistic conditions of the play involved. Arguing that part of the appeal of such dream worlds is the polymorphous perversity with which they scramble identity and character, the author traces the postindustrial milieux from which such fantasies have arisen in postwar Japan and been popularly received in the United States.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.002
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1040.034

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations239
Published2019
Admission routes1
Has abstractyes

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Same topicGothic Literature and Media AnalysisFrench-language works237,207