Social categorization and math stereotyping (replication using new images)
Bibliographic record
Abstract
The first purpose of this study is to replicate our previous study investigating how people spontaneously categorize Asian women, versus White men, when completing a math/arts IAT that allows these targets to be categorized by race, gender, or both. The second goal is to examine whether categorization (by race, gender, or both) results in stereotype-consistent associations. Specifically, are people who appear to be categorizing these faces primarily by gender less likely to associate Asian women with math compared to those who appear to be categorizing primarily by race? An Implicit Association Test (IAT) will be used to examine these questions. In the IAT, participants will be presented with Asian female faces and White male faces and will be asked to categorize these faces using a header showing both race and gender (i.e., “Asian Woman” cartoon image “White Man” cartoon imaes). In order to determine how people are primarily categorizing these faces, we will add 3 additional categorization trials seamlessly to the end of the measure. These will be selected from four images that include Asian men (n=2) and White women (n=2) of comparable age and attractiveness. If participants are attending primarily to race, they should sort these with the Asian woman and White man header, respectively. By contrast, if participants are attending primarily to gender, we should see the reverse pattern – Asian men should be sorted with the White Man header, and White women should be sorted with the Asian Woman header. Based on our previous studies, we anticipate that the majority of participants will categorize by gender and that they will show a math-gender stereotype on this implicit measure, despite the female images being exclusively of East-Asian women. Unlike our previous study with a similar design, we are making use of a new set of images from " Beaupré, M.G., & Hess, U. (in press). Cross-cultural emotion recognition among Canadian ethnic groups." Journal of Cross-cultural Psychology. (with one Asian female face replaced due to make-up; and taken from Utrecht ECVP). All images were also cropped with an oval so that only the main parts of the face appear. We plan to collect 160 participants.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".