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Queer Tax: Examining 2SLGBTQ+ Black, Indigenous, and People of Colour's experiences of accessing assisted reproductive technologies

2025· article· en· W4409328925 on OpenAlexafffundabout
Michelle Tam, Amaya Perez‐Brumer, Lori E. Ross

Bibliographic record

VenueSocial Science & Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of TorontoPublic Health Ontario
FundersSocial Sciences and Humanities Research Council of CanadaLesbian Health Fund
KeywordsQueerIndigenousSociologyGender studies

Abstract

fetched live from OpenAlex

The use of assisted reproductive technologies (ART) has risen steadily over the past two decades. In Canada, up to 25 % of assisted reproductive technologies (ART) users identify as Two-Spirit, lesbian, gay, bisexual, trans, and/or queer (2SLGBTQ+). Despite evidence of inequitable ART access for 2SLGBTQ+ communities, scant research has explored the intersectional experiences of 2SLGBTQ+ Black, Indigenous, and People of Colour (BIPOC). Theoretically grounded in reproductive justice and critical political economy, this study examines ART access and clinical experiences for 2SLGBTQ+ BIPOC communities. Interviews were conducted with BIPOC and 2SLGBTQ+ people who had undergone or were seeking ART in Ontario, Canada. Data analysis, guided by constructivist grounded theory and situational analysis, was coded using MAXQDA. The findings reveal structural powers and systemic inequalities shaping the ART process and parenthood. Participants identified four key barriers faced by 2SLGBTQ+ BIPOC families: (1) normative practices (re)produced through ART; (2) mandatory counselling as gatekeeping and disciplining; (3) regulation of known donor sperm augmenting legal, financial, and timeliness barriers; and (4) limited availability of Black, Indigenous, and People of Colour donor sperm. These intersectional barriers highlight the urgent need for ART providers to offer competent and inclusive care. Additionally, the study underscores the necessity for clinical policy reforms to challenge heteronormative and racist practices, ensuring equitable access and improving availability of BIPOC donor sperm for diverse family structures.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.005
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.370
Teacher spread0.330 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes3
Has abstractyes

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