Seeing gender stereotypes: The role of second-order head/facial features
Bibliographic record
Abstract
Human faces serve as windows to the intricated realm of social perceptions and interactions. We often rely on stereotypes to quickly frame people by looking at their faces. Focusing on the gender-science stereotype (GSS), we explored the visual weight of face/head features in attributing a specific STEM (science, technology, engineering and mathematics) competence, or lack thereof. The aim is to study basic mechanisms underlying gender stereotypes and the perceptual and cognitive dynamics that come into play when activating or enhancing GSS. Starting from a female head and a male head, a set of female (Exp.1) and a set of male heads (Exp.2) were created by modifying second-order features (i.e., beard, make-up, hair colour and length). A scientific and a literary text were associated to the heads of the two sets; participants’ (N= 90) task was to evaluate the likelihood of the text’s authorship on a 10-point Likert scale based on the head visualized. Results show a negative effect for blonde hair (‘’dumb blonde’’ effect) for both genders, and for heavy make-up for females; a positive effect was found for glasses for both genders, and for beard for males. A Linear Mixed Model Analysis was run to explore the differences between scores for female and male heads. Results show that male heads received significantly higher scores in Science than female ones (p < 0.001), but scores relating to Literature were statistically equal (p=0.82). Overall, results suggest the existence of a gender bias. Results and interactions will be discussed in light of the perceptual elements at play to understand the interplay between cognitive and perceptual factors in activating the GSS.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".