Intimate Image Dissemination and Consent in a Digital Age: Perspectives from the Front Line
Bibliographic record
Abstract
Abstract Media attention on nonconsensual intimate image dissemination has led to the relatively recent proliferation of academic research on the topic. This literature has focused on many areas including victimization and perpetration prevalence rates, coerced sexting, legal and/or criminal contexts, sexual violence in digital spaces, gendered constructions of blame and risk, and legal analysis of high-profile cases and legislation. Despite this research, several gaps exist, including a lack of empirical research with service providers. Informed by in-depth interviews with 10 sexual violence frontline professionals in Southern Ontario (Canada), this chapter focuses on their perspectives of the additive role of technology. With respect to nonconsensual intimate image dissemination, technology acts as a digital “layer” that operates in addition to the commission of physical acts of sexual violence, and compounds the harms experienced by the victim by adding a virtual – and indelible – “permanent remembering” of the violence. Nuancing the contours of consent in a digital age, this chapter concludes by considering what consent means in a technological context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".