MétaCan
Menu
← Back to cohort
Record W2171023411 · doi:10.32920/ryerson.14655411.v1

LGBTQ Immigrant Exclusion: An Introduction

2021· preprint· en· W2171023411 on OpenAlexaffabout
Johanna Laing

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLesbianMainstreamQueerMulticulturalismAgency (philosophy)Settlement (finance)ImmigrationPublic relationsSociologyService providerGender studiesIdentity (music)Political scienceService (business)BusinessMarketingSocial scienceLawPedagogy

Abstract

fetched live from OpenAlex

Based upon personal experience, existing literature, original data from Toronto 211 and key informants, this research paper identifies the need for the development and implementation of settlement services that meet the needs of lesbian, gay, bisexual, trans and queer (LGBTQ) immigrants and newcomers. These sources of evidence suggest that these especially marginalized immigrants and newcomers receive a cool and minimal welcome and their service options are limited to a very select few agencies that may only meet some, not all, of their needs, and may exacerbate identity conflicts. This paper argues the importance of providing a wider range of settlement agency options to LGBTQ migrants through the intergration of LGBTQ services into both mainstream and culturally specific settlement agencies. With a critical eye to transferability to a multicultural and multi-faith sector, recommendations for ways in which settlement agencies can build or improve their accessibility and services for LGBTQ clients are inspired by research into 'culturally competent and 'safe space' practices discussed in existing academic and practical literature.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.004
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.064
GPT teacher head0.432
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicHomelessness and Social Issues→French-language works237,207→