Constructing Masculinity in Women’s Retailers: An Analysis of the Effect of Gendered Market Segmentation on Consumer Behavior
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".