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Record W4408868040 · doi:10.3998/mpub.14405362

Queer Throughlines

2025· book· en· W4408868040 on OpenAlexfundno aff
Ju Hui Han

Bibliographic record

VenueUniversity of Michigan Press eBooks · 2025
Typebook
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
FundersCenter for the Study of Women, University of California, Los AngelesSocial Sciences and Humanities Research Council of CanadaCenter for East Asian Studies, Stanford UniversityUniversity of ChicagoUniversity of California, IrvineMax-Planck-Institut zur Erforschung Multireligiöser und Multiethnischer GesellschaftenJohns Hopkins UniversityWellesley CollegeUniversity of MichiganGeorge Washington UniversityUniversity of PennsylvaniaOregon State UniversityPrinceton University
KeywordsHistory

Abstract

fetched live from OpenAlex

Queer Throughlines draws on years of direct participation, interviews, and ethnography to examine transnational Korean LGBTQ+ activism since the 1990s. Han maps the sites and routes of leftist and queer political movements, highlighting challenges posed by Christian conservatives in both South Korea and the United States. The book uses the concept of "throughlines" to weave together a web of movement stories across time and space: a coalition of Los Angeles–based LGBTQ+ activists and allies fighting an anti-gay petition campaign led by Korean immigrant churches; queer activists involved in anti-war protests in Seoul; progressive clergy embracing inclusivity and risking heresy charges and excommunication; and queer and trans activists refusing to be sidelined from visions of political change underway. These moments do not always line up in a straightforward narrative of victory or progress, yet they create powerful lines of solidarity, community, and kinship.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.106
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0050.005
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1060.018

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 head0.031
GPT teacher head0.252
Teacher spread0.220 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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