Reframing the sex debate in scientific research: three frameworks to support rigor without rigidity
Bibliographic record
Abstract
Debates about the nature of sex are prominent in scientific, scholarly and public discourses. Scientific as well as societal debates largely focus on whether sex is appropriately conceptualized as a binary, a spectrum, or some other form. These have taken on particular urgency because of highly contested, polarizing, and consequential developments in the global landscape in which the political weaponization of binary sex has escalated. Here we propose three frameworks useful for retaining focus on why sex might be conceptualized in a certain way to address the aims of a given project to help avoid pitting binary versus non-binary approaches against one another in a continued impasse. These three frameworks are (a) sex as a system of classification, (b) sex as a trait, and (c) sex as a dynamic system. We also provide a series of questions and considerations to guide a critical read of scholarship and scientific engagement of sex in ways that might help mitigate the isolating effects of disciplinary silos and advance cross-disciplinary dialogue and collaboration. Thus, rather than recapitulating discussions of how sex should be conceptualized or trying to establish a universally applicable definition of sex, these three frameworks and guiding questions can be drawn on to consider why certain projects might employ particular definitions and methods of operationalization of sex over others, and outline the merits and drawbacks of each. By refocusing our scholarly attention on what sex is empirically being employed to do and why, and providing these frameworks to think with, we hope to bring some essential bridging between those for and those against binary thinking about sex and foster a diversity of approaches that might more effectively build on one another rather than exist in opposition. We can then continue to try to nimbly engage what it is sex is being leveraged to do and why in order to ask how a given conceptualization will help us to learn about a given phenomenon.
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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 | 0.595 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.029 | 0.305 |
| Scholarly communication | 0.062 | 0.054 |
| Open science | 0.016 | 0.051 |
| Research integrity | 0.030 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 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".