The rise of digisexuality: therapeutic challenges and possibilities
Bibliographic record
Abstract
Radical new sexual technologies, which we term “digisexualities,” are here. As these technologies advance, their adoption will grow, and many people may come to identify themselves as “digisexuals” – people whose primary sexual identity comes through the use of technology. Researchers have found that both lay people and clinicians have mixed feelings about digisexualities. Clinicians must be prepared for the challenges and benefits associated with the adoption of such sexual technologies. In order to remain ethical and viable, clinicians need to be prepared to work with clients participating in digisexualities. However, many practitioners are unfamiliar with such technologies, as well as the social, legal, and ethical implications. Guidelines for helping individuals and relational systems make informed choices regarding participation in technology-based activities of any kind, let alone ones of a sexual nature, are few and far between. Thus, a framework for understanding the nature of digisexuality and how to approach it is imperative.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".