Sex and the Riddle of Variability
Bibliographic record
Abstract
Scientific research associations, funders, and publishers have recently introduced sex inclusion mandates requiring the use of male and female specimens in preclinical research designs and the analysis and reporting of data disaggregated by sex. However, it is not necessarily a simple matter to incorporate males and females in the same study design with the aim of detecting differences between them while following best practices for rigorous inference in laboratory science using model organisms. For example, if there are ways in which male and female variability might differ for the trait or procedure of interest, principles of sound experimental design may require larger numbers of organisms and observations to make valid inferences about the presence of a sex difference. This paper analyzes a current scientific debate over differences in variability between male and female laboratory rodents, and specifically over whether potential sources of sex-specific variability such as the estrous cycle, group housing, and body size constitute components of sex that should be measured. The variability debate surfaces the trade-offs between constructs of sex difference and similarity that face scientific researchers attempting to meet mandates to include both males and females in research design and report sex-specific results. This "riddle of variability" illuminates how laboratory researchers using model organisms must make contextual choices (Richardson 2022) at multiple decision points in order to stabilize sex as a biological variable in a particular research design. These judgments are informed by social and epistemic values and carry consequences for the validity, precision, and generalizability of claims of biological sex differences derived from preclinical research models.
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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 | 0.166 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.098 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".