More than a sex crime: a feminist political economy of the 2014 iCloud hack
Bibliographic record
Abstract
This article examines the media framing of and relations to the 2014 iCloud hack, wherein hundreds of female celebrities' private photos were stolen and distributed online. In particular, I problematize the reading of this event as merely signalling the misogyny of 'toxic' online cultures and contextualize it as part of a larger political economy of female celebrity. I argue that, while the growth in feminist discourses emanating from both the mainstream media and celebrity women is encouraging, it perhaps occludes the broader power relations that extend across both new and traditional media, ensuring maintenance of the status quo. This event exemplifies problems with a popular form of feminism that seeks inclusion into these systems, rather than wider systemic change. Therefore, in addition to examining the celebrity and/or her audience as the site of political (feminist) work, I call for an excavation of the systems in which she is embedded and her relations to the means of media production and profit.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".