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Record W4312920071 · doi:10.1109/hri53351.2022.9889580

Fluid Sex Robots: Looking to the 2LGBTQIA+ Community to Shape the Future of Sex Robots

2022· article· en· W4312920071 on OpenAlexaff
Skyla Y. Dudek, James E. Young

Bibliographic record

Venue2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · 2022
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRobotHuman sexualityHeteronormativityPerspective (graphical)Identity (music)SociologyGender studiesComputer scienceArtificial intelligenceAesthetics

Abstract

fetched live from OpenAlex

As sex robots continue to be developed by industry, portrayed by media, and studied by researchers, it is common to conceptualize robots from a cisgender and heterosexual (cishet), or feminist perspective. We advocate for an increased shift toward the 2LGBTQIA+ community for inspiration and a path forward for more inclusive, successful, and socially responsible sex robots. In addition to the intrinsic value of being inclusive, looking to the 2LGBTQIA+ community can help us to break away from traditional ideas of gender and sexuality, to unlock the full potential of this technology to be flexible and offer new possibilities. Further, we reflect on the importance of considering how the designs of sex robots, as politically charged technological artifacts, can contribute to reinforcing ideas about heteronormativity; instead, sex robots have the potential to positively contribute to breaking down traditional barriers surrounding gender and sex. We envision a future of sex robots that reach their full potential as fluid, individualized companions that enable people to comfortably engage their interests and identity.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0080.016
Scholarly communication0.0110.013
Open science0.0020.013
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0170.003

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.

Opus teacher head0.205
GPT teacher head0.451
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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