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Record W4385807832 · doi:10.1080/10538720.2023.2246918

Realities of LGBTQ + people with intellectual disabilities: A narrative review of the literature through the lens of recognition theory

2023· review· en· W4385807832 on OpenAlexafffund
Élise Milot, Ann‐Sophie Otis, Kévin Lavoie, Martin Caouette, Julie Beauchamp

Bibliographic record

VenueSexual and Gender Diversity in Social Services · 2023
Typereview
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContemptNarrativePsychologyOpenness to experienceDiversity (politics)Queer theoryIntellectual disabilityGender studiesSociologyPrejudice (legal term)Disability studiesNarratologySocial psychologyQueer

Abstract

fetched live from OpenAlex

This article presents the results of a narrative review of the literature on the realities of LGBTQ + people with intellectual disabilities (ID). Twelve papers were analyzed. The data were extracted and interpreted through the lens of Honneth’s recognition theory. This review shows that more situations of contempt are experienced by LGBTQ + people with ID than situations of recognition. Experiences of violence, infantilization and discrimination, among others, are reported by those who participated in the surveyed studies. The analysis of the results made it possible to better understand their reality and to make recommendations. In particular, it is suggested that sex education programs for persons with ID be optimized so that issues relating to sexual diversity and gender plurality are addressed. It is also necessary to equip carers to increase their openness and comfort level in addressing these issues.

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)0.005
metaresearch head score (Gemma)0.010
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.162
GPT teacher head0.380
Teacher spread0.218 · 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
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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