Exploring Experiences of Safety With LGBTQ+ Newcomers in Calgary, Alberta
Bibliographic record
Abstract
ABSTRACT Lesbian, gay, bisexual, transgender, queer/questioning, and gender and sexual diverse (LGBTQ+) newcomers arrive in Canada, a country renowned as a “safe haven” for those escaping anti‐LGBTQ+ policies. Despite Canada's reputation, notions of safety are not guaranteed as LGBTQ+ newcomers continue to face systems of oppression as they navigate their new country of residence. Drawing from the feminist affect literature, this study sought to understand how LGBTQ+ newcomers navigate and perceive safety. This study employed an arts‐based method called participatory community mapping as well as semistructured interviews to explore six participants' experiences in Calgary, Canada. The findings of this study suggest the complex and vast experiences of LGBTQ+ newcomers upon settlement. LGBTQ+ newcomers face various challenges, such as discrimination, and navigating cultural differences alongside new systems. While adverse experiences were identified, participants also described spaces that elicit a sense of safety. Entering spaces free of judgment, where one can feel authentic to oneself, and fostering community and a sense of belonging, are critical facets of experiencing safety. Several mechanisms were employed to navigate safety, including concealing one's identity, avoiding diasporic communities, or trusting one's instinct. Feeling safe is not static and inherent in various spaces, but rather, requires complex negotiations with other people and considerations for one's LGBTQ+ identity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".