Is gender primacy universal?
Bibliographic record
Abstract
Emerging evidence suggests that gender is a defining feature of personhood. Studies show that gender is the primary social category individuals use to perceive humanness and the social category most strongly related to seeing someone—or something—as human. However, the universality of gender’s primacy in social perception and its precedence over other social categories like race and age have been debated. We examined the primacy of gender perception in the Mayangna community of Nicaragua, a population with minimal exposure to Western influences, to test whether the primacy of gender categorization in humanization is more likely to be a culturally specific construct or a cross-cultural and potentially universal phenomenon. Consistent with findings from North American populations [A. E. Martin, M. F. Mason, J. Pers. Soc. Psychol. 123, 292–315 (2022)], the Mayangna ascribed gender to nonhuman objects more strongly than any other social category—including age, race, sexual orientation, disability, and religion—and gender was the only social category that uniquely predicted perceived humanness (i.e., the extent to which a nonhuman entity was seen as “human”). This pattern persisted even in the most isolated subgroup of the sample, who had no exposure to Western culture or media. The present results thus suggest that gender’s primacy in social cognition is a widely generalizable, and potentially universal, 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".