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Record W4313172464 · doi:10.1123/jsm.2022-0044

Fashion Versus Comfort: Exploring the Gendered Marketing Messages of Sport Team Licensed Merchandise

2022· article· en· W4313172464 on OpenAlexaff
Katherine Sveinson, Larena Hoeber

Bibliographic record

VenueJournal of Sport Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsClothingFandomMasculinityFemininityPerformative utteranceAdvertisingConsumption (sociology)PsychologySports marketingMarketing communicationIdentity (music)MarketingSociologySocial psychologyBusinessGender studiesRelationship marketingPolitical scienceMedia studiesMarketing managementAesthetics

Abstract

fetched live from OpenAlex

Sport team licensed merchandise plays an important role in sport fan experiences. Existing work has explored how consumers perceive these items, motivation and consumption habits, and intent to purchase. Extending upon the performative sport fandom framework, this study explores the marketing messages of sport fan merchandise, and the resulting implied gendered and fan performances. Employing a multimodal critical discourse analysis, we analyzed the top 20 T-shirts for men and women for five National College Athletic Association institutions on their official ecommerce sites. By examining the text descriptions, visual images, and messages perceived when combining text and visuals, we found that the marketing messages of clothing items rely heavily on traditional discourses of femininity and masculinity, placing gender performances as more relevant to fan performances for women. Atypical designs suggest alternative gender and fan performances but continue to indicate that gender identity is central to clothing appearance and messaging to consumers.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.297
Teacher spread0.231 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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