Nothing new here: Emphasizing the social and cultural context of deepfakes
Bibliographic record
Abstract
In the last year and a half, deepfakes have garnered a lot of attention as the newest form of digital manipulation. While not problematic in and of itself, deepfake technology exists in a social environment rife with cybermisogyny, toxic-technocultures, and attitudes that devalue, objectify, and use women’s bodies against them. The basic technology, which in fact embodies none of these characteristics, is deployed within this harmful environment to produce problematic outcomes, such as the creation of fake and non-consensual pornography. The sophisticated technology and metaphysical nature of deepfakes as both real and not real (the body of one person, the face of another) makes them impervious to many technical, legal, and regulatory solutions. For these same reasons, defining the harm deepfakes causes to those targeted is similarly difficult and very often targets of deepfakes are not afforded the protection they require. We argue that it is important to put an emphasis on the social and cultural attitudes that underscore the nefarious use of deepfakes and thus to adopt a more material-based approach, opposed to technological, to understanding the harm presented by deepfakes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".