Advancing quantitative intersectionality research methods: Intracategorical and intercategorical approaches to shared and differential constructs
Bibliographic record
Abstract
A range of methods have recently been proposed for incorporating intersectionality theoretical frameworks into quantitative research methodology. We published a pair of articles on methods for intercategorical intersectionality, in which we distinguished analytic from descriptive studies, identified causal mediation decomposition methods as an appropriate strategy for analytic intercategorical intersectionality, and introduced and validated a group of three new measures of major, day-to-day, and anticipated discrimination for use in intercategorical analysis (Bauer and Scheim, 2019; Scheim and Bauer, 2019). We respond to points raised in four invited commentaries on our original pair of articles-by Evans (2019); Jackson and VanderWeele (2019); Harnois and Bastos (2019); and Richman and Zucker (2019). We discuss differential constructs, which represent those that exist only for those at particular intersections, or for which meanings vary across intersections. Whereas such constructs may be studied intracategorically, in intercategorical studies they place limits on both the types of measures that may be used and the statistical analyses that can be conducted. Most quantitative intersectionality methods work has therefore focused on intracategorical measures, but intercategorical analyses. We present a preliminary agenda for continued methods development in quantitative intersectionality methods.
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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 | 0.531 | 0.584 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".