The Problem of Consent with Teledildonics and Adult Webcam Platforms
Bibliographic record
Abstract
In this article, I examine some of the dangers that are associated with sex toys known as teledildonics. Unlike more conventional sex toys, teledildonics connect to the internet and allow their users and others to control these devices remotely and often through a Bluetooth connection. While teledildonics introduce new ways of engaging and experiencing sexual pleasure, they do so by risking the personal and sensitive data that such devices transmit and collect from their users. Moreover, I consider the risk that teledildonics pose as connected technologies that can be hacked and controlled, scrutinizing what this means in terms of consent and sexual assault in intimate relationships and on a live adult webcam platform like Chaturbate. I investigate how current legal definitions of consent and sexual assault neglect online sex workers, and especially those who work within a tip and token system like Chaturbate, and question how legal protections can be enforced amidst the jurisdictional and territorial problems that plague cyberspace more broadly. With these lack of protections in place, I build on scholarly research that identifies some of the risks that are associated with teledildonics as technologies of potential sexual assault (Nixon 2018; Sparrow and Karas 2020; Arrell 2022). In specific, I study how Canadian laws are ill-equipped to address the more obscure nature of consent and sexual assault as they pertain to Chaturbate and Lovense devices, a leading teledildonics company.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".