Touching at a Distance: Digital Intimacies, Haptic Platforms, and the Ethics of Consent
Bibliographic record
Abstract
The last decade has seen rise in technologies that allow humans to send and receive intimate touch across long distances. Drawing together platform studies, digital intimacy studies, phenomenology of touch, and ethics of technology, we argue that these new haptic communication devices require specific ethical consideration of consent. The paper describes several technologies, including Kiiroo teledildonics, the Kissenger, the Apple Watch, and Hey Bracelet, highlighting how the sense of touch is used in marketing to evoke a feeling of connection within the digital sphere. We then discuss the ambiguity of skin-to-skin touch and how it is further complicated in digital touch by remediation through platforms, companies, developers, manufacturers, cloud storage sites, the collection and use of data, research, satellites, and the internet. Lastly, we raise concerns about how consent of data collection and physical consent between users will be determined, draw on examples in virtual reality and sex-robotics, and ultimately arguing for further interdisciplinary research into this area.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.065 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.121 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".