Makeover media as fashion journalism: <i>What Not To Wear</i> , fashion, authority, and <i>Gonzo</i> subjectivity
Bibliographic record
Abstract
The USA-based television program What Not To Wear (WNTW) was a staple of popular fashion media, informing audiences about acceptable modes of dress and appearance. We consider how aspects of this show and its accompanying book encompass features of traditional fashion reportage – particularly advice literature – and also approaches to fashion communication that overlap with the style and concerns of “New Journalism” (those modes of reporting – sometimes called “Gonzo” – that emphasize informality, emotional engagement, and an interest in “real” people and “real” lives). By examining the text, images, and talk deployed by the book and the TV show, we indicate how WNTW perceives, constructs, and conveys the fashioned subject in ways that link makeover media to broader contexts of cultural commentary.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 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".