More than a buzzword: how intersectionality can advance social inequalities in health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it