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Voices From Women's Wardrobes: Mid-Life and Self-Image

2017· report· en· W2885313778 on OpenAlexaff
Maria R. DalCin, Sandra Tullio-Pow

Bibliographic record

Venuenot available
Typereport
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsClothingEmbodied cognitionPerceptionNegotiationPhotovoicePsychologySociologyGender studiesSocial psychologyAestheticsVisual artsArtPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

Current fashion research has not explored adult women's perceptions of available clothing choices in relation to changes they experience during midlife. Physical and cultural shifts such as menopause, divorce, or the premature death of a partner may significantly disrupt a woman's disposition. In response, clothing may become a vital expression of their personal and public personas. To better understand embodied dress practice, the ways that women in mid-life negotiate the fashion system to develop their self-image were investigated using Photovoice. Women (n=11) between the ages of 45-55, took full-body selfies dressed in their favourite daywear outfits and discussed the photographs in a semi structured interview. Results were categorized according to common themes: past and present fashion influences, shopping behaviours, wardrobe building strategies, as well as common strategies used to navigate the fashion system in order to establish a wardrobe that reflected their self-image. Findings are important to fashion designers, retailers and marketers.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0050.002
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.271
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 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
Published2017
Admission routes1
Has abstractyes

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