How Those with Close Connections with LGBTQ2S+ Talk About That Community
Bibliographic record
Abstract
There has been a steady increase in North America of people who identify as LGBTQ2S+. In a 2017 survey done by GLAAD, 20% of millennials identify as LGBTQ2S+. This increase would seem to increase the odds that soon we are all going to have someone close to us who is LGBTQ2S+. With that in mind, this research was done with the idea that having someone close to you that identifies as LGBTQ2S+ could affect the way you talk about that community. Critical discourse analysis was used through the lens of qualitative research, the use of a focus group and a qualitative interview from convenience sampling were done, and from those data collection techniques, language, behaviours, attitudes, and perceptions of the participants were analyzed. While many studies focus on the negative attitudes and language of society, this study took the approach of starting from the positive aspects of having a close relationship and being a support to someone who is LGBTQ2S+. Due to the nature of my subject matter, I chose to use gender neutral pseudonyms, gender neutral pronoun references and refrained from gender binary language. Department: Sociology Faculty Mentor: Dr. Kalyani Thurairajah
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".