Artificial Emotions and Love and Sex Doll Service Workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Realistic looking humanoid love and sex dolls have been available on a somewhat secretive basis for at least three decades. But today the industry has gone mainstream with North American, European, and Asian producers using mass customization and competing on the bases of features, realism, price, and depth of product lines. As a result, realistic life size artificial companions are becoming more affordable to purchase and more feasible to patronize on a service basis. Sexual relations may be without equal when it comes to emotional intimacy. Yet, the increasingly vocal and interactive robotic versions of these dolls, feel nothing. They may nevertheless induce emotions in users that potentially surpass the pleasure of human-human sexual experiences. The most technologically advanced love and sex robots are forecast to sense human emotions and gear their performances of empathy, conversation, and sexual activity accordingly. I offer a model of how this might be done to provide a better service experience. I compare the nature of resulting “artificial emotions” by robots to natural emotions by humans. I explore the ethical issues entailed in offering love and sex robot services with artificial emotions and offer a conclusion and recommendations for service management and for further research.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it