“But There’s a Million Jokes About Everybody . . .”: Prevalence of, and Reasons for, Directing Negative Behaviors Toward Gay Men on a Canadian University Campus
Bibliographic record
Abstract
The purpose of this study was to assess the frequency and types of negative behaviors directed toward gay men on university campuses and to understand heterosexual men's and women's motivations for engaging in antigay discrimination. Using a mixed methods approach, results from a quantitative survey (N = 286) indicated that students primarily engaged in covert antigay behaviors, such as telling antigay jokes and spreading gossip about gay men. Follow-up qualitative interviews with 8 highly homonegative individuals (4 men, 4 women) were then conducted to better understand their self-perceived motivations for perpetrating antigay discrimination. Results indicated that antigay behaviors were conducted to reinforce traditional male gender roles, alleviate feelings of discomfort, and convey heterosexual identity. Participants also expressed concern about being perceived as prejudiced and were motivated to control their prejudicial reactions to some degree. Implications regarding the contemporary nature of antigay violence on university campuses are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".