‘Cyber warfare’ in style: Cambridge Analytica and a mediatized ethics of fashion
Bibliographic record
Abstract
On 29 November 2018, Christopher Wylie – a former Ph.D. researcher in fashion trends and the whistleblower of Cambridge Analytica – delivered a speechto The Business of Fashion’sVOICESconference, detailing how the firm harvested the data of more than 87 million Facebook users and utilized fashion psychographic brand preferences as measurable and estimable data that could be manipulated to influence political opinion through targeted psychographic social media content. This article takes Cambridge Analytica as a point of departure to explore interstices between fashion brands and consumers, and entrenched surveillance, in a mediatized field of fashion and within the culture wars. Jodi Dean’s concept of communicative capitalism, in which consumer statements take the form of affective bursts disseminated into and captured via the network under a market logic, articulates how our desires and/as identities can be honed to alter democratic processes and outcomes. Extending an affect framework, I posit that formations of consumer or user affect characterize relations between fashion companies and consumers in a politicized climate. This paper issues a call to scrutinize issues of media ethics, discourse and surveillance that these cases raise via a fashion studies perspective that perceives aesthetic preference as formative and reflective of global politics.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".