Picture a scientist: classification images of scientists are perceived as White, male, and socially inept
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
Introduction: Stereotypes and biases toward social categories are often reflected in mental representations of faces. The current study used a two-phase reverse correlation procedure to visualize mental representations of the faces of a scientist, a hero, a genius, and a person. Methods: In the first phase, 20 participants completed four blocks of a two-image forced-choice task. In each block, they selected which face from a pair resembled one of the four categories. The images they selected were averaged to create classification images (CIs), which serve as proxy images for their mental representations of the four categories. In the second phase of the study, 251 naive participants rated the CIs based on various valence and demographic characteristics. Results: We found that the scientist image was rated predominantly as White and male, which reflects stereotypes about who pursues scientific careers. The scientist image was also rated more negatively than the other CIs on several characteristics, which may indicate negative biases toward scientists as unsociable, poor communicators, and incompetent authority figures, especially during the COVID-19 pandemic. Discussion: These findings deepen our understanding of how social categories are represented and demonstrate how the CI method can reveal stereotypes and attitudes related to these social categories.
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.
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 | 0.001 | 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) | 0.002 | 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