Approaches to Sampling Gay, Bisexual, and Other Men Who Have Sex with Men from Geosocial-Networking Smartphone Applications: A Methodological Note
Bibliographic record
Abstract
Geosocial-networking smartphone applications utilize global positioning system (GPS) technologies to connect users based on their physical proximity. Many gay, bisexual, and other men who have sex with men (MSM) have smartphones, and these new mobile technologies have generated quicker and easier modes for MSM to meet potential partners. In doing so, these technologies may facilitate a user’s ability to have multiple concurrent partners, thereby increasing their risk for acquiring HIV or other sexually transmitted infections. Researchers have sought to recruit users of these applications (e.g., Grindr, Jack’d, Scruff) into HIV prevention studies, primarily through advertising on the application. Given that these advertisements often broadly targeted large urban areas, these approaches have generated samples that are not representative of the population of users of the given application in a given area. As such, we propose a method to generate a spatially representative sample of MSM via direct messaging on a given application using New York City and its geography as an example of this sampling and recruitment method. These methods can increase geographic representativeness and wider access to MSM who use geosocial-networking smartphone applications.
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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 | 0.078 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".