How Do Institutional Pressures Shape the Experiences of Plus-Size Consumers in the Fashion Markets of Canada and Iran?
Bibliographic record
Abstract
This study investigates the experiences of plus-size consumers in the fashion markets of Cana-da and Iran, highlighting some of the exclusionary practices within the industry that limit con-sumer access to fashionable and well-fitting clothes. Through a qualitative approach mix of in-depth interviews and netnography, this research explores how coercive, normative, and mimetic institutional pressures form the experiences of plus-size people within these two diverse mar-kets. This study analyzes these pressures to show how cultural, economic, and political ele-ments combine to exclude plus-size consumers, especially in the non-Western contexts of Iran, where economic sanctions make market limitations even worse. This is important because plus-size consumers face great difficulties in general, and plus-size men often do not get attention when it comes to fashion research. Moreover, this research underlines the role of representation in marketing: the visibility of plus-size models can positively influence the self-esteem and con-fidence of consumers. This paper adds to the existing literature on consumer exclusion and fashion through a comparative analysis between the plus-size fashion markets of Canada and Iran. The contributions of this research are at once theoretical, in developing the intersection of institutional theory and consumer exclusion, and practical, with implications for fashion brands, policymakers, and advocates of inclusivity. This study highlights the call for body size inclusiv-ity and shows how one-size-fits-all in plus-size fashion cannot fulfill the many needs that plus-size varied shapes may require. It thus advocates for an approach to fashion that is more inclu-sive and diverse through uptake in custom-designed pieces, increased marketing representation, and policy reforms that will ensure access to these excluded consumers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".