Are There Two Sexes? Yes and No, But Mostly No (and Gender is Something Else Anyway – More or Less)
Bibliographic record
Abstract
This paper brings a species-inclusive, biologically grounded lens to the question, are there two and only two sexes? Insofar as the terms associated with sex are used to pick out taxa where reproduction is typically achieved through the fusion of two gametes of different sizes, the answer is yes. Insofar as the terms associated with sex are used to pick out morphs within a species, the answer is often no, though the question is an empirical one and must be addressed species by species. Within our own species, where we have species-typical primary and secondary sex characteristics that usually align with gametic differences, there are many naturally occurring developmental differences that do not so align. Gender, though often confused with sex, is something else altogether, being a sociocultural kind rather than a biological one. However, because the social roles and norms associated with a particular gender are frequently assigned on the basis of a sex ascription, gender is often experienced as inextricably entwined with sex. Moreover, in cultural animals, gender and sex traits are generally the result of the interactions between biological and social causes. I conclude that the idea that there are two and only two sexes in our own species is simply false, as is the idea that gender can be reduced to secondary sex characteristics.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".