Bibliographic record
Abstract
The settlement services sector in Toronto, Canada has faced difficulties in responding to lesbian, gay, bisexual, trans, and queer (LGBTQ) immigrant youth in ways that respect their specific experiences. One way agencies have taken up this challenge in Toronto has been to develop LGBTQ-specific settlement services. Housed within a diverse range of organisations, these services are intended to engage and support LGBTQ immigrant youth. In this article, we report on evaluation research conducted with LGBTQ immigrant young people from Griffin Centre’s reachOUT Newcomer Network where we asked about their experiences accessing settlement services in Toronto. Our findings suggest that LGBTQ immigrant youth are deeply influenced by intersecting identities linked to racialization, sexuality, gender identity, education, employment, and immigration status. Participants expressed overwhelming interest in accessing support, but remain disconnected from settlement services. A reconceptualization of LGBTQ settlement services within a framework of hybridity that challenges essentialism and neo-colonialism would improve service delivery. This shift would allow for more integrated settlement services that acknowledge LGBTQ newcomer youth and their experiences of (un)belonging.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.062 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".