Virtually Naked: Virtual Environment Reveals Sex-Dependent Nature of Skin Disclosure
Bibliographic record
Abstract
The human tendency to reveal or cover naked skin reflects a competition between the individual propensity for social interactions related to sexual appeal and interpersonal touch versus climatic, environmental, physical, and cultural constraints. However, due to the ubiquitous nature of these constraints, isolating on a large scale the spontaneous human tendency to reveal naked skin has remained impossible. Using the online 3-dimensional virtual world of Second Life, we examined spontaneous human skin-covering behavior unhindered by real-world climatic, environmental, and physical variables. Analysis of hundreds of avatars revealed that virtual females disclose substantially more naked skin than virtual males. This phenomenon was not related to avatar hypersexualization as evaluated by measurement of sexually dimorphic body proportions. Furthermore, analysis of skin-covering behavior of a population of culturally homogeneous avatars indicated that the propensity of female avatars to reveal naked skin persisted despite explicit cultural norms promoting less revealing attire. These findings have implications for further understanding how sex-specific aspects of skin disclosure influence human social interactions in both virtual and real settings.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".