The impact of leader’s support and leader’s sexual orientation on inclusion of gay men in the workplace: an experimental study
Bibliographic record
Abstract
A leader who models inclusive behavior can contribute significantly toward the creation of a supportive environment for diverse members. To date, studies identifying how leader’s support for inclusion of gay men and how leader’s sexual orientation can influence inclusion of gay men are scarce. With a scenario based experimental study, I examined whether leaders’ support for inclusion of gay men and leaders’ sexual orientation have an impact on followers’ decision to hire a gay man and follower’s attitudes toward gay men co-workers. Hypotheses were tested on a sample of 149 full-time working adults of mixed occupations using two-way analysis of variance. The results showed followers are more likely to hire a gay man when their leader supports inclusion of gay men than when their leader does not support inclusion of gay men in the workplace. Results also suggested that followers are less likely to hire a gay man employee and are less likely to have positive attitudes toward their gay men co-workers when their leader is a gay man compared to when their leader is a heterosexual man. The implications of these findings are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".