Preventing, Reducing and Ending LGBTQ2S Youth Homelessness: The Need for Targeted Strategies
Bibliographic record
Abstract
Gender non-conforming and sexual minority youth are overrepresented in the homeless youth population and are frequently discriminated against in shelters and youth serving organizations. This paper provides a contextual understanding of the ways that institutional and governmental policies and standards often perpetuate the social exclusion of lesbian, gay, bisexual, transgender, queer, and 2-Spirit (LGBTQ2S) youth, by further oppression and marginalization. Factors, including institutional erasure, homophobic and transphobic violence, and discrimination that is rarely dealt with, addressed, or even noticed by shelter workers, make it especially difficult for LGBTQ2S youth experiencing homelessness to access support services, resulting in a situation where they feel safer on the streets than in shelters and housing programs. This paper draws on data from a qualitative Critical Action Research study that investigated the experiences of a group of LGBTQ2S homeless youth and the perspectives of staff in shelters through one-on-one interviews in Toronto, Canada. One of the main recommendations of the study included the need for governmental policy to address LGBTQ2S youth homelessness. A case study is shared to illustrate how the Government of Alberta has put this recommendation into practice by prioritizing LGBTQ2S youth homelessness in their provincial plan to end youth homelessness. The case study draws on informal and formal data, including group activities, questions, and surveys that were collected during a symposium on LGBTQ2S youth homelessness. This paper provides an overview of a current political, social justice, and public health concern, and contributes knowledge to an under researched field of study by highlighting concrete ways to prevent, reduce, and end LGBTQ2S youth homelessness.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".