Behind the Scenes with Louise Dahl-Wolfe and Toni Frissell: Alternative Views of Fashion Photography in Mid-Century America
Bibliographic record
Abstract
This essay explores the process and labour involved in creating fashion editorials. It is focused on the work of Louise Dahl-Wolfe and Toni Frissell, as case studies of photographers who worked at America’s two leading fashion magazines: Harper’s Bazaar and Vogue. Images that show these women “backstage” form the basis of this analysis, to expose the images’ compositions and the teams of people involved in their creation. Both photographers worked at a key moment in American fashion, as designers such as Claire McCardell created a simple, interchangeable wardrobe of readymade clothes that catered to the increasingly active lives of middle-class women. They were significant to the “Modern Sportswear Aesthetic” that emerged during this period and which exploited Kodachrome’s rich tones to compose alluring images that showed sportswear as adaptable and fashionable. Frequently shot outside, or using carefully contrived sets, their imagery provides a case study for the ways fashion’s creative workers collaborated to construct convincing visions of sportswear’s emergent style. Drawing upon Bruno Latour’s theories of organization, this article examines these networks of people, working to varied briefs and deadlines to create each magazine issue. From contact sheets and shots of fashion editors and models, to glimpses of the photographers’ efforts to find the right angle, this essay uses Dahl-Wolfe and Frissell’s photobooks and archival materials, including memos between Bazaar Editor-in-Chief Carmel Snow and Frissell, to challenge the idea of the seamless fashion page and look at the professional work and negotiations necessary to create a successful image.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.033 | 0.039 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".