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Record W4321451674 · doi:10.1080/15240657.2022.2133523

The Matrixial Gaze: Transgender in Boys Don’t Cry

2022· article· en· W4321451674 on OpenAlexaff
Sheila L. Cavanagh

Bibliographic record

VenueStudies in Gender and Sexuality · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicDiverse academic research themes
Canadian institutionsYork University
Fundersnot available
KeywordsTransgenderPhallic stagePsychoanalytic theoryGazeSubjectivityQueer theoryPsychoanalysisGender studiesIdentity (music)PsychologyTemporalityRelevance (law)SociologyQueerAestheticsEpistemology

Abstract

fetched live from OpenAlex

This article brings the feminist psychoanalysis of Bracha L. Ettinger to a reading of Kimberley Pierce’s landmark film Boys Don’t Cry. While many transgender studies scholars have critiqued the film from an intersectional lens, few have engaged important questions relevant to a transgender gaze from a feminist psychoanalytic angle. Feminist psychoanalytic theory offers insight into the gaze, the mirror, gender, sexual difference, temporality, trauma, and memory of relevance not only to cisgender women but to transgender subjects irrespective of gender identity. Ettinger’s formulation of the Other Sexual Difference (OSD) provides a way to understand elements of trans- experience that are not visible, yet significant to subjectivity. I contend that there is a correspondence between what Ettinger calls the matrixial gaze and the transgender gaze operating in the film that helps us to understand the interhuman dimensions of looking irreducible to identity. Both feminist psychoanalytic theory and transgender studies are concerned not only with gender but with elements of being that are not ocular and are too often eclipsed in phallic and white cisgender representational practices.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.014
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.460
GPT teacher head0.523
Teacher spread0.063 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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