Perceiving ingroup and outgroup faces within and across nations
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
The human face is arguably the most important of all social stimuli because it provides so much valuable information about others. Therefore, one critical factor for successful social communication is the ability to process faces. In general, a wide body of social cognitive research has demonstrated that perceivers are better at extracting information from their own-race compared to other-race faces and that these differences can be a barrier to positive cross-race relationships. The primary objective of the present paper was to provide an overview of how people process faces in diverse contexts, focusing on racial ingroup and outgroup members within one nation and across nations. To achieve this goal, we first broadly describe social cognitive research on categorization processes related to ingroups vs. outgroups. Next, we briefly examine two prominent mechanisms (experience and motivation) that have been used to explain differences in recognizing facial identities and identifying emotions when processing ingroup and outgroup racial faces within nations. Then, we explore research in this domain across nations and cultural explanations, such as norms and practices, that supplement the two proposed mechanisms. Finally, we propose future cross-cultural research that has the potential to help us better understand the role of these key mechanisms in processing ingroup and outgroup faces.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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