Exploring digit ratio and handedness in asexual and allosexual individuals
Bibliographic record
Abstract
Asexuality, often characterized as an absence of sexual attraction, is now understood as a spectrum incorporating varying levels of sexual and romantic attraction. Despite growing visibility and scientific interest, asexuality remains stigmatized and often misunderstood as a valid sexual orientation. While previous research has identified biomarkers such as digit ratios (2D:4D) and handedness in the context of homosexuality, their association with asexuality remains underexplored. To address this gap and contribute to the understanding of asexuality as a distinct sexual orientation, the authors conducted an online study recruiting asexual ( n = 366) and non-asexual (allosexual; n = 1,305) participants. They collected digit ratio measurements using scans of both right and left hands and assessed handedness using the Edinburgh Handedness Inventory. Results indicated that across all sexual orientations, females had higher left- and right-hand digit ratios than males, consistent with most prior research. Also, consistent with some prior research, exploratory analyses suggested asexual men had elevated rates of non-right-handedness relative to allosexual men. In addition, the authors found nuanced differences, such that sexual orientation, handedness, and the hand used for digit ratio calculations significantly interacted. They found that non-right-handed asexual participants had a lower right-hand-digit ratio than those attracted to more than one gender. Also, non-right-handed asexual participants had a significantly lower left-hand-digit ratio than heterosexual and gay/lesbian participants. Overall, these findings contribute to the limited body of literature on asexuality and highlight the importance of considering the potential complex interaction of multiple biological/prenatal factors in the development of sexual orientation, including asexuality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".