Operationalization, Measurement, and Interpretation of Sex/Gender
Bibliographic record
Abstract
Abstract Given the proliferation of calls to consider sex and gender in biomedicine, it is critical to address how the two concepts, and the relationships between them, are being implemented in a research setting. This chapter considers how we might transcend a simple, binary female–male framing and embrace the idea of the entanglement of sex and gender. The ways that the terms sex and gender are typically used in biology and health research are considered, with a focus on the relationships between these constructs, and areas of coherence and disagreement in their conceptualization. Problems arise when sex and gender are principally operationalized in terms of a female–male binary, including not only the resulting exclusion of trans, nonbinary, and intersex individuals but also the inadequacy of a binary analytical framework to account for context, overlap, in-group heterogeneity, continuity, and similarity. Entanglement and interaction are compared and contrasted, three forms of scientifc engagement with these ideas are identifed, and the implications of intersectionality for the operationalization of sex and gender are considered. In the context of experimentation, an entanglement perspective on sex and gender is explored for what it might enable along with the challenges it presents. As researchers grapple with the incorporation of sex and gender in their work, these frameworks will require ongoing development and refnement, reduced reliance on the dominant binary female–male analytical framing, and a move to a contextual, mechanistic approach that better refects conceptual complexity, diverse
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".