Uncovering gender discrimination cues in a realistic setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Which face cues do we use for gender discrimination? Few studies have tried to answer this question and the few that have tried typically used only a small set of grayscale stimuli, often distorted and presented a large number of times. Here, we reassessed the importance of facial cues for gender discrimination in a more realistic setting. We applied Bubbles-a technique that minimizes bias toward specific facial features and does not necessitate the distortion of stimuli-to a set of 300 color photographs of Caucasian faces, each presented only once to 30 participants. Results show that the region of the eyes and the eyebrows-probably in the light-dark channel-is the most important facial cue for accurate gender discrimination; and that the mouth region is driving fast correct responses (but not fast incorrect responses)-the gender discrimination information in the mouth region is concentrated in the red-green color channel. Together, these results suggest that, when color is informative in the mouth region, humans use it and respond rapidly; and, when it's not informative, they have to rely on the more robust but more sluggish luminance information in the eye-eyebrow region.
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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 it