From Rags to Riches, the Policing of Fashion and Identity: Governmentality and “What Not To Wear”
Bibliographic record
Abstract
Even the most casual perusal of television over the past ten years should reveal an increasing number of self-improvement reality shows. This paper explores the Learning Channel (TLC) television show What Not to Wear (WNTW), which provides fashion advice to deviant dressers. We use Foucault's concept of governmentality to understand how WNTW engages women in their own projects of self-improvement in ways that are simultaneously disciplining and pleasing. Women who participate in the show are taught by the hosts, Stacy and Clinton, how to view themselves through the gaze of an imagined middle-class public. We suggest that WNTW tells us that outward appearances are the privileged site from which identities and self can be read. Even though the goal of the show is not to change identities, many of the women claim to experience a radical transformation. These transformations are often in the direction of a new professional and feminine identity, one maintained within the structure of the show by the continuing possibility and internalization of surveillance.
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 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 | 0.001 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".