Social Media and Safe Spaces: A Mixed Methods Study on Identity Formation for LGBTQ+ Albertans
Bibliographic record
Abstract
Within literature pertaining to race and LGBTQ2IA+ identities, much of our current research is situated within a universalized hegemony of placing Whiteness, heterosexuality, and cisgender as the default, both in terms of daily experiences and conceptions of safety. The purpose of this research is to develop a more holistic understanding of LGBTQ2IA+ life as conceptualized in locations without visible role models or communities, in order to create better inclusion and representation within LGBTQ2IA+ resources in Alberta. This inclusivity must be separate from that of the ideations of metronormativity, wherein the existence of LGBTQ2IA+ lives outside cities with large LGBTQ2IA+ populations like New York, are erased. The research details the importance of community and representation, the role of technology as an identity construction site, and a specific focus on trans and POC identities as experienced simultaneously, rather than additive. Through research conducted online with an embedded mixed methods survey containing open and closed ended questions, key questions arise in regard to how sexuality, gender, and geographical location intersect to produce specific experiences online and offline for LGBTQ2IA+ Albertans. Understanding how identity is developed through online platforms for individuals that are geographically isolated, and the ways in which homophobia, transphobia, and racism are uniquely experienced in a more rural Canadian setting, highlight the need for better visibility, openness, and education regarding identity and the importance of a community that has practical and genuine applications of inclusivity.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".