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Record W3080250916 · doi:10.38055/fs010112

Re-Dressing Race and Gender: The Performance and Politics of Eldridge Cleaver’s Pants

2018· article· en· W3080250916 on OpenAlexaffvenue
Art M. Blake

Bibliographic record

VenueFashion Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPoliticsContext (archaeology)Gender studiesSociologyQueerPower (physics)AestheticsHistoryArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

In Paris in 1975 Eldridge Cleaver, exiled revolutionary African American activist, former Minister of Information for the Black Panther Party, appeared in photographs and newspaper articles wearing, and discussing, pants he had designed. The major innovation in Cleaver’s pants was a redesigned crotch: instead of the usual button and zip front opening, his pants featured a soft panel with a protuberant fabric appendage into which Cleaver intended the wearer’s penis to fit. Why did Cleaver channel his intelligence and creativity into menswear at that moment? How did Cleaver’s penis-positive pants design resonate in 1975 with black politics and gender politics? And why am I, a queer transgendered man, writing about these pants? Through this article I hope to contribute to a discussion in fashion studies about the materiality of bodies and the role of self-fashioning, particularly for those living in resistance to dominant codes of gender and race. I situate and analyze Cleaver’s pants in a broad context of the postwar politics of dressing and redressing race and gender in the United States, with references to a longer American history, as well as to a global context of clothing in a postcolonial era. The pants, in both their design and in the act of being worn, materialize acts of raced and gendered insurrection, but in a web of historical power relations that privilege whiteness and cisgender masculinity.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.127
GPT teacher head0.312
Teacher spread0.185 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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