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Record W3081124432 · doi:10.38055/fs020102

Entanglements of a Dress Named Laverne: Threads of Meaning between Humans and Things (and Things)

2019· article· en· W3081124432 on OpenAlexaffvenue
J. S. Kennedy, Megan Strickfaden

Bibliographic record

VenueFashion Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPropositionNarrativeInterdependenceClothingAestheticsMeaning (existential)SociologyAgency (philosophy)Identity (music)EpistemologyArtHistoryPhilosophyLiterature

Abstract

fetched live from OpenAlex

This is a narrative about a dress named Laverne and a woman named Elizabeth told through Ian Hodder’s proposition about entanglements. Elizabeth Withey wrote a blog called Frock Around the Clock about her lived experience of wearing the black dress “Laverne” every day for one year. Through Hodder’s three themes of entanglement — humans depend on things, things depend on things, and things depend on humans — the interdependencies between a woman and a dress are uncovered. Laverne is a thread within a web of other threads of entanglement driven by her relationship with a person. This is demonstrated through Hodder’s illustration of sequential staging and the vast network of things required for Laverne’s existence. Laverne and Elizabeth’s interdependent relationship is further developed through a close examination of their interactions, including how Laverne is reliant on Elizabeth to acquire and maintain agency. In turn, Elizabeth finds comfort during a tumultuous year by constructing and reconstructing her identity with Laverne as a kind of transitional object. Our discussion concludes by offering three general insights into the entangled and complex human-clothing relationship. The complexities of a human relationship and interactions with a dress are exposed in this case study through an in-depth dive into slow fashion that reveals significant insights into the entangled relationships and interactions people have with clothing.

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 score0.196
Threshold uncertainty score0.424

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 studies0.0000.000
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 head0.062
GPT teacher head0.286
Teacher spread0.224 · 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
Published2019
Admission routes2
Has abstractyes

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