A Model Subject: Problematizing the 'Beauty Sells' Ideal in Fashion Marketing
Bibliographic record
Abstract
Since the introduction of mannequins and models in fashion, the standard of beauty in fashion has been thin, tall, white, and conventional bodies, representing the normative ideals of perfection of the time. This Major Research Project challenged the assumption in fashion marketing of how 'beauty' sells; in essence, how clothing has to be draped on a conventionally beautiful and thin model to appear attractive and marketable to the consumer. Using body mapping as a unique arts-based methodology, the study observed the affective response of three participants in St. John's, Newfoundland to two series of fashion advertisements portraying beauty standards versus othered bodies. The resulting body maps expressed that today's consumers want to feel recognized and validated by advertisements — they need to be able to see aspects of themselves in fashion media in order to truly relate to the brand in question. This research contributes to the field of fashion marketing by offering a method of increasing consumers' positive attitudes towards brands, thus leading to greater brand equity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".