More than a buzzword: how intersectionality can advance social inequalities in health research
Bibliographic record
Abstract
Intersectionality is increasingly adopted in research to understand the complex ways that social inequalities shape health. Intersectional research thus explores how multiple forms of oppression intersect and shape how marginalised social groups experience health issues. Yet intersectionality research has often neglected to focus on the upstream structural factors that (re)produce social inequalities in health. In this paper, we argue that intersectionality can further advance social inequality in health research when it is used to understand more than just the multiplicity of socially marginalised groups’ experiences and identities, but also how interlocking social structures and power relations perpetuate social inequalities in health. We suggest that analysing policy with an intersectional lens is a key entry point to empirically explicate the underlying mechanisms that permit social inequalities in health to persist. To illustrate our argument, we use the example of how an intersectional perspective can be adopted to better understand the role of tobacco control policies in contributing to social inequalities in smoking.
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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 | 0.104 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.019 | 0.110 |
| Scholarly communication | 0.038 | 0.055 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".