Conversations with Scholars of American Popular Culture: Elizabeth Wissinger
Bibliographic record
Abstract
Elizabeth Wissinger is a Professor at the City University of New York Graduate School and Center, as well as BMCC, where she teaches Fashion Studies and Sociology. Her research focuses on technology, fashion, and embodiment. Professor Wissinger has lectured on topics related to gender and race, media, bodies, labor, and fashion in the US, Canada, Australia, and Europe. She is also the co-author of the edited volume, Fashioning Models: Image, Text, and Industry (Berg, 2012) with Joanne Entwistle. Her research has earned several grants and awards, including Mellon Fellowships in both the Humanities and Science Studies. Her current research takes up the issue of how wearable technology genders bodies.We discussed her new book This Year's Model: Fashion, Media, and the Making of Glamour (New York University Press, 2015).------------What drew you to the study of fashion?Fashion is a power structure that organizes so many aspects of life in need of analysis: patriarchy, feminist issues, embodiment, labor issues, globalization, and social justice. I had been exposed to the world in my early years in NYC, and during my graduate training I became fascinated by the notion that fashion studies was a field.As I note in the book, the proliferation of selfie-obsessed, #nofilter culture, the pressure for ordinary people to try to be fashionable has been spreading beyond young girls duckfacing in the bathroom. Fashion has become a lifestyle for so many, I wanted to know more about why.When people copy Kim K’s selfie for example, they are driving an image economy, but who profits? Facebook and Instagram. The fun of it all lures us into selling ourselves. Tweeting about or posting one’s latest physical accomplishments, posting a selfie of one’s newly enhanced butt, slimmed waist, or latest outfit pulls one’s bodily potential and connectivity into metering and regulation an availability that facilitates capital’s constant expansion. At the same time, the very act of posting, puts one’s quotient on the line, rising and falling by the metrics of likes, hearts, influence scores, and views. Keeping the quotient high becomes a sort of compulsion, the labor to stay visible and relevant - to matter.What inspired you to write this book?I was interested in issues surrounding women’s power and came to realize that having lived as part of the New York fashion scene as a young woman gave me a unique perspective on the most iconic figures of this world: fashion models. Whether you hate to love them, or love to hate them, fashion models are a key element of popular culture. Their work is embroiled in debates about controversial bodily ideals.They are at the center of struggles for social power and acceptance, and they figure prominently in conflicts between men and women. Needless to say, fashion modeling is a hot topic that pushes many buttons.For all its glamour, modeling’s dark underbelly has been well documented. For instance, you may have seen Girl Model - a documentary about scouting for teen girls in Siberia, and the deplorable conditions they sometimes find themselves in in Japan - or J’Amais Assez Maigre - French model Victoire Dauxerre’s account of her struggle to keep her health and sanity while working as a model. Debates center on whether images of skinny models cause eating disorders or damage young girls’ self esteem. This conflict has been ongoing since the 1970s, which is significant as I discuss in my book.What is glamour labor?Glamour labor is a phenomenon of the internet age. I hit on the idea of labor when trying to explain modeling work. A key process in modeling is constructing one’s “look.” The model “look” comprises the model’s appearance in person, and all the images in which the model has appeared. The “look” marries the physical body and the virtual self into one and was helpful in understanding the idea of affective labor, which drew me to study models. …
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".