#HotForBots: Sex, the non-human and digitally mediated spaces of intimate encounter
Bibliographic record
Abstract
Contemporary practices of sex and intimacy are increasingly digitally mediated. In this paper, we identify two distinctly spatial effects of these mediations. First, the digital extends the spaces of sex/uality beyond the immediately proximate, simultaneously expanding the potential for non-human object choice in intimate encounters. Second, the digital intensifies the experiential fidelity of intimate encounters by folding the remote into the spatially immediate, such that non-proximate intimate relations with human subjects as well as non-human objects may feel more proximate. We articulate these effects by building on and contributing to developments in the geographies of encounter, which allows us to bring together theories and conceptual framings of intimacy, digitality and sexuality in a uniquely spatial register. These effects of extension and intensification resonate in a selection of empirical examples of digitally mediated sex/uality that we place along continuums of more-and-less human and more-and-less proximate. These continuums comprise the conceptual axes of a heuristic framework that we advance to both (i) capture particular points at which configurations of spaces, practices and subject/object choices of sex crystallize given conditions of pervasive digital mediation, and (ii) provoke further interrogations of the multiple ways in which sex, sexuality and intimacy are recast by the digital.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".