Bibliographic record
Abstract
Eyelashes evolved to protect eyes. An optimum eyelash length functions to protect eyes from external hazards such as contaminations, excessive evaporation or shear stress from airflow. They can also be an indicator of a person's health as various congenital and noncongenital diseases can lead to short or long eyelashes. The current study aimed to extend a recent investigation on the preference for eyelash length in humans from an evolutionary adaptive perspective. Specifically, the current study tested whether the inverted-U function for eyelash length preference recently reported for White faces, generalises to other ethnicities, and whether ethnic background modulates preference for eyelash lengths. To investigate this question, men and women of Asian, Black, and White ethnicities from the U.S. rated the attractiveness of female Indian, Asian, Black, and White faces with varying eyelash lengths. The eyelashes ranged in length from no eyelashes to half the width of an eye. Results showed that Asian, Black, and White men and women preference for eyelash length followed an inverted-U function across all four ethnicities, supporting a general preference for human eyelash length that is approximately one-third the width of an eye. In addition, the results showed that the most attractive eyelashes for Black women were skewed toward a greater eyelash-length to eye-width ratio when compared to the other images. The source of this skew is presently unknown, as it could reflect a change in perceptual sensitivity to eyelash length with skin colour or changes in preference related to perceptions of participants' ethnicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".