???You???re really cute for a black guy??? - a mixed methods approach to sexual racism on gay dating applications
Bibliographic record
Abstract
Sexual Racism is the covert form of racial prejudice enacted in the context of sex or \nromance. This is apparent within online dating spaces, specifically among gay dating \napplications like that of Grindr. There is a large amount of literature on gay dating apps, \nspecifically Grindr, however most of this literature focuses on issues outside of race. The \npresent study focuses on the intersections of racism and dating relationships among gay \nmen of color. This study uses a blended quantitative and qualitative survey to gather data \nregarding experiences of discrimination on gay dating applications among 100 men of \ncolor across the United States and Canada. It focuses on how gay men of color navigate \ngay dating apps and how these apps shape their relationships and sexuality. Findings \nsuggest that racism in gay dating apps can negatively affect how gay men of color \nnavigate these online spaces and cope with their experiences of discrimination. The study \nconcludes that many participants experience various forms of discrimination and the \ntoxicity that plagues gay dating applications has got to change as soon as possible.
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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 | 0.066 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".