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Record W4406397030 · doi:10.1002/dvr2.70004

Exploring Experiences of Safety With LGBTQ+ Newcomers in Calgary, Alberta

2025· article· en· W4406397030 on OpenAlexafffundabout
Thomas Tri, Ajwang’ Warria

Bibliographic record

VenueDiversity & Inclusion Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Calgary
FundersMitacsUniversity of Calgary
KeywordsQueerGender studiesSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.009
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.277
GPT teacher head0.482
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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