Conflicts of Interest, Culture Jamming and Subversive (S)ignifications: The High Fashion Logo as Locational Hip hop Artic
Bibliographic record
Abstract
In 2012, a fashion line called “Conflict of Interest NYC” (C.O.I.) released a collection of three unisex t-shirts: each one took the brand name and/or logo of a storied fashion house and imposed a set of urban and hip hop references to subvert the brand’s refined, Eurocentric connotations. This article describes the t-shirts’ function as parodies using media studies accounts of culture jamming as political practice; Linda Hutcheon’s formulation of parody as literary method; and Henry Louis Gates’s outline of Signifyin(g) in the black oral vernacular tradition. It further contextualizes the t-shirts within historical tensions between dominant fashion institutions and hip hop culture at the semiotic level of the brand logo. It then reads the t-shirt containing the name BALLINCIAGA (manipulating the Paris fashion house Balenciaga) for its intertextual and historical relations to the expressive forms and urban locations of hip hop culture. Finally, the article examines a photograph of a fashion editor wearing the BALLINCIAGA t-shirt to London Fashion Week, in October 2012, as a moment of resistance but also of fashion re-appropriating the t-shirts’ political content. Still, the t-shirts’ circulation as commodities permits for reflection on the politics of fashion and subcultures within the dominant logic of capitalism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".