Toronto Ethnically Diverse face database: a multi-faceted stimulus set
Bibliographic record
Abstract
Face stimuli are often used in psychological and neuroimaging research to assess perceptual, cognitive, social, and emotional processes. Many available face databases, however, have limited diversity in ethnicity, emotional expression, gaze direction, and/or pose, which constrains their utility to specific contexts. Having a diverse face database can mitigate these biases and may help researchers investigate novel topics that examine the effects of ethnicity on these processes. The Toronto Ethnically Diverse (TED) face database is designed to provide an open-access set of 271 unique White, Black, East-Asian, South-Asian, South-East Asian, Middle Eastern, Multi-racial, and Indigenous adult models. The TED database includes diversity in race, gender, pose, gaze direction, and three emotion variations (neutral, open-mouth happiness, closed-mouth happiness). Validation data of the stimuli based on judgments of the emotional expressions showed high inter-rater reliability and high accuracy as measured by proportion correct and Cohen's kappa scores. Intensity, and genuineness ratings are also presented for each model. The validation results for TED suggest that this face database consists of models displaying their intended emotions with high fidelity. This database will be useful to researchers seeking to study underrepresented groups and to other broad groups of researchers who are studying face perception.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".