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Record W1971659783 · doi:10.1017/s002191181200126x

Introduction to the<i>JAS</i>at AAS Roundtable on “Sexuality and the State in Asia”

2012· article· en· W1971659783 on OpenAlexaboutno aff
Elizabeth J. Remick

Bibliographic record

VenueThe Journal of Asian Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipHuman sexualityChinaCeremonyState (computer science)Asian studiesGender studiesInclusion (mineral)Presidential systemPresidential addressPolitical scienceSocial scienceMedia studiesSociologyHistoryLawPoliticsPublic administration

Abstract

fetched live from OpenAlex

Attendees of the 2012 Association for Asian Studies (AAS) annual conference in Toronto were treated to two extraordinary speeches at the presidential address and awards ceremony. First, Charlotte Furth's acceptance of the AAS Award for Distinguished Contributions to Asian Studies was a primer in the history of China-related gender studies (Furth 2012). Then, as Rachel Leow discusses in her paper in this forum, outgoing AAS President Gail Hershatter followed up with an inspirational critique (reprinted in this issue) of the current state of gender and sexuality studies in China. Taken together, these two speeches showed how far gender and sexuality studies in Asia have come in the last forty years, but also suggested that it is time for some fresh approaches. For example, Furth explained that when the Cambridge History of China volumes on Republican China were commissioned, she and others argued strenuously for the inclusion of a chapter on gender; but in the end, one could not be written because no one had yet done the scholarship on which such a chapter could be based. Fortunately, all of this has changed: the scholarship is there now. But Hershatter quite rightly pointed out that it is time to rethink many of the categories of analysis we have been using, because they are preventing us from asking questions we should be asking, and therefore making us miss the meanings of crucial social events and phenomena.

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)0.006
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 head0.052
GPT teacher head0.343
Teacher spread0.291 · 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 designQualitative
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

Citations0
Published2012
Admission routes1
Has abstractyes

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