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Record W3043268118 · doi:10.38055/fs010104

Enclothed Knowledge: The Fashion Show as a Method of Dissemination in Arts-Informed Research

2018· article· en· W3043268118 on OpenAlexaffvenue
Ben Barry

Bibliographic record

VenueFashion Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnonymityEmbodied cognitionThe artsQualitative researchResearch ethicsSociologyPsychologyPublic relationsSocial psychologyComputer sciencePolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

In this article, I investigate the processes, benefits, and dilemmas of producing a fashion show as a method of dissemination in arts-informed qualitative research. I examine a project that used a fashion show to analyze and represent interview findings about men’s understandings and performances of masculinities. Fashion shows facilitate the dissemination of new qualitative data — what I coin “enclothed knowledge” — that is embodied and inaccessible through static or verbal descriptions. Fashion shows also enable participants to shape knowledge circulation and allow researchers to engage diverse audiences. Despite these benefits, researchers have to be mindful of ethical dilemmas that occur from the absence of anonymity inherent in public performances; therefore, I suggest strategies to mitigate these threats to research ethics. Ultimately, I argue that fashion shows advance social justice because the platform can transform narrow, stereotypical understandings of marginalized identities.

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)0.028
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
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 head0.781
GPT teacher head0.743
Teacher spread0.038 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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