Fighting for inclusion across borders: Latin American Trans women’s health in Canada
Bibliographic record
Abstract
Background: Worldwide, Trans women from Latin America experience some of the highest rates of violence, which has led many to emigrate. There is limited research exploring the experiences of Trans migrants, and most LGBTQI2S + migrant research focuses on immigrant gay men. This study uses the frameworks of Intersectionality and the Social Determinants of Health (SDoH) to examine the impact of migration on the health and wellbeing of Latin American Trans women living in Toronto, Canada. Methodology: This qualitative arts-based study included nine participants and used hand mapping, a sociodemographic questionnaire, and focus groups to generate data. Data analysis encompassed inductive and deductive approaches and rigor was maintained through reflexivity and several verification strategies. Results: While migration was used as a safety strategy, participants' multiple identities as immigrants, Trans women, and Latinas, produced compounded experiences of oppression post-migration. Facing transphobia and xenophobia simultaneously, participants were forced to navigate precarious housing and employment, minimal social capital, and low social position. This limited their ability to exercise power and ultimately caused poor health and wellbeing post-migration; however, participants used sophisticated strategies to resist asymmetrical power relations, actively searching for safety and community participation, and caring for themselves and each other. Conclusion: The participants fought for inclusion across borders of economic exclusion and gender identity, borders of power and social position, as well as geopolitical borders. Their intersectional experiences across these "borders" should be understood in the context of migration without liberation, consumption without income, compounding oppressions, as well as positive intersectionality. While the women's resistance and strength are positive by-products of fighting oppression, they cannot be the solution. Access to health and wellbeing should not be a privilege for some; it must be a right for all.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".