Dating app use and everyday discrimination among bisexual and heterosexual individuals
Bibliographic record
Abstract
Mobile dating applications (“dating apps”) have become popular, particularly among sexual minority groups (e.g., lesbian, gay, bisexual, pansexual, and other sexual orientations), potentially due to limited opportunities to meet similar others offline. Experiences of discrimination in everyday life (i.e., not constrained to dating apps, such as being treated with less respect or courtesy than others based on membership in a particular group) are common among sexual minority groups, and this factor has received little empirical attention in relation to dating app use. In this secondary data analysis ( Lenton-Brym et al., 2021 ), data from 243 adults who completed online questionnaires about the extent of one’s dating app use were analyzed. Bisexual participants reported greater everyday discrimination overall compared to heterosexual participants. Frequency of everyday discrimination was positively associated with the extent of dating app use when controlling for age, gender, and race/ethnicity, but only for heterosexual and not bisexual individuals. Findings suggest that bisexual participants use dating apps regardless of discrimination experiences in everyday life, whereas heterosexual individuals are more likely to use dating apps with increased perceived discrimination, potentially due to other factors unrelated to sexual orientation, age, gender, and race/ethnicity, such as physical appearance. Seventeen percent of the variation in dating app use scores was explained by the statistical model. Further exploration of factors that contribute to one’s extent of dating app use is needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".