Where’s the tech in sex research? A brief critique and call for research
Bibliographic record
Abstract
Internet and data-based technologies are ubiquitous in most societies around the world. People use online technologies (i.e., devices, software, platforms, applications, etc., that connect to the Internet through wired or wireless means) in almost all aspects of their daily lives, including sexuality. Yet, researchers have been slow to integrate online technologies in sexuality studies. The purpose of this paper is to briefly review the opportunities and challenges associated with integrating research about online technology with research about human sexuality. We argue that researchers focused on (almost) all topics of human sexuality would benefit from considering online technologies in their studies. We describe how people’s online and in-person experiences do not exist in separate vacuums; rather, they influence and are influenced by one another in an ongoing and dynamic fashion. We propose three ways that sexuality researchers can integrate technology and technology-informed research in their future studies that address some of the opportunities and challenges: adding variables and constructs, using technology-focused theories, and collaboration.
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How this classification was reachedexpand
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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".