Sex-Specific Finger-Length Patterns Linked to Behavioral Variables: Consistency across Various Human Populations
Bibliographic record
Abstract
In humans, as in nonhuman primates, the digits of the hands are similar in length during early fetal development. Subsequently, differentiation leads to a patter of unequal finger lengths, described by George as the finger-length pattern. Recent work by Manning and colleagues suggested that digit length patterns are due to early influences of sex hormones. Most importantly for psychology, such patterns might also relate to cognitive activities that are influenced by early organizing actions of sex hormones. The exciting possibility of having an easily measurable indicator of early action of sex hormones that relates to behavior led us to examine the universality of digit length patterns. With samples from Brazil, Canada, India, Turkey, and Korea, we showed that patterns of distal extent of finger tips are similar across different human populations. Consistent sex differences were found across the samples, showing that the index finger in males extends less far distally relative to the middle finger than is the case for females and that the difference in distal extent between index and ring fingers, relative to the middle finger, is smaller in females than in males.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".