Clickable Consent: How Men Who Have Sex with Men Understand and Practice Sexual Consent on Dating Apps and in Person
Bibliographic record
Abstract
Smartphone-based dating applications like Grindr are popular among men who have sex with men (MSM), and it is common for MSM to engage in sexual activity with other users. Despite this, there is limited research on MSM's negotiations of consent for online sexual interactions and in-person sexual encounters. This study examined MSM's understandings and practices of consent on dating apps and in person. Interpretative phenomenological analysis of 25 interviews with MSM dating app users revealed that many participants could identify key aspects of consent but did not always apply those understandings to their own practices. For online sexual interactions, some participants viewed consent as connecting to a dating app - a practice I term "clickable consent" - while other participants viewed consent as continuous and communicated consent in explicit and implicit ways. Although all participants negotiated consent online in preparation for an in-person sexual encounter, some renegotiated consent in person with explicit or non-explicit communication, while others did not renegotiate consent in person. Results shed light on how MSM's online conversations impact their in-app and in-person sexual activities, and reveal challenges that MSM face in digital and physical spaces. Conclusions, implications, and suggestions for future research are discussed further.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".