Bibliographic record
Abstract
This article provides an interdisciplinary and intersectional analysis of sex robots and/as sexual fantasy. I demonstrate that sexual fantasy is a highly complex and salient vector of analysis for any discussion of love and sex with robots. First, I introduce contemporary North American sex robots and offer a brief sketch of their ontology as relates to sex toys and pornography. Next, I provide a short but instructive mapping of sexual fantasy scholarship from across the fields of experimental psychology, media and cultural studies, post-colonial, psychoanalytic, feminist, queer and critical race theory. My goal here is to demonstrate sexual fantasy’s polymorphous and productive nature and its complex relationship to reality. Drawing on the theories of sexual fantasy canvassed herein, I examine the role of fantasy to sex robots’ inception, marketing, and consumption. From here I offer an appraisal of radical feminist, new materialist, and disabled queer and trans feminists’ critiques of sex robots and their users. I argue that theorizing sex robots through the lens(es) of sexual fantasy is necessary given efforts to stigmatize, regulate, and criminalize sexual fantasy and sextech users in the post/digital age. Future scholarship is encouraged to further examine the sex robot/sexual fantasy nexus and to consider whether and how their intersections impede or facilitate the development of alternative “networks of affection” including those that lie between the platonic and romantic or between “carbonsexuality” and technosexuality/digisexuality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".