Gender and sexual minorities: intersecting inequalities and health
Bibliographic record
Abstract
Purpose – The purpose of this paper is to outline the use of intersectionality theory in research with gender and sexual minorities – that is, with lesbian, gay, bisexual, trans, and queer (LGBTQ) people, and lesser-studied groups such as two-spirited people. Design/methodology/approach – First, the paper note the limited way that LGBTQ research has taken up issues of intersecting oppression. The paper outlines why theoretical and methodological attention to overlapping oppressions is important, and why theorists of intersectionality have identified the additive model as inadequate. The paper presents a sketch of current best practices for intersectional research, notes special issues for intersectional research arising within qualitative and quantitative paradigms, and finishes with an overview of how these issues are taken up in this special issue ofEthnicity and Inequalities in Health and Social Care. Findings – Current best practices for intersectional research include. Bringing a critical political lens to data analyses; contextualizing findings in light of systemic oppressions; strategically using both additive and multivariate regression models; and bringing a conscious awareness of the limitations of current methods to our analyses. Originality/value – This paper addresses the use of intersectionality theory in research with gender and sexual minorities, highlighting methodological issues associated with qualitative and quantitative paradigms in LGBTQ research.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".