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Record W2951782188 · doi:10.18778/1733-8077.15.1.04

Constructing Masculinity in Women’s Retailers: An Analysis of the Effect of Gendered Market Segmentation on Consumer Behavior

2019· article· en· W2951782188 on OpenAlexaffabout
Eric Filice, Elena Neiterman, Samantha B. Meyer

Bibliographic record

VenueQualitative Sociology Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMasculinityFemininitySociologyGender studiesConsumption (sociology)Context (archaeology)Hegemonic masculinityDoing genderHeterosexualitySocial constructionismPerformative utteranceDialecticSocial psychologyPsychologyHuman sexuality

Abstract

fetched live from OpenAlex

While gender-based differences in consumer behavior have been previously investigated within the context of gender-neutral or unisex retailers, men’s behavior in women’s retailers remains largely unexplored. Furthermore, most studies frame the retail environment as a passive platform through which essential gender differences yield setting-specific bifurcated behavior, and do not address the role the commercial establishment and men’s shopping habits play in gender identity formation and maintenance. To address this gap, we analyzed men’s behavior in women’s retailers using interactionist and social constructionist theories of sex/gender. Data were collected through non-participatory observation at a series of large, enclosed shopping malls in South-Western Ontario, Canada and analyzed thematically. We found that men tend to actively avoid women’s retailers or commercial spaces that connote femininity, while those who enter said spaces display passivity, aloofness, or reticence. We suggest the dominant cultural milieu that constitute hegemonic masculinity— disaffiliation with femininity, an accentuation of heterosexuality, and a prioritization of homosocial engagement—nform the dialectical relationship between individual and institutional gender practice that manifests through consumption.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.442
Teacher spread0.370 · 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
Published2019
Admission routes2
Has abstractyes

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