“See, I’m not racist!”: Aversive Racism, Peer Pressure, and Blaming Adolescents
Bibliographic record
Abstract
Abstract\nThis study examined how peer pressure influences participant’s attribution of blame to Black or White youth committing a crime. Participants read one of four scenarios in which a Black or White male (Kevin), who was or was not under peer pressure, stole a bicycle. To measure the amount of blame participants assigned to the adolescent, they completed a blame attribution inventory. Participants also completed a personality scale to measure their perceptions of the adolescent’s personal characteristics. To avoid being perceived as prejudiced, the researchers predicted participants would blame the White adolescent more than the Black adolescent for stealing the bicycle. Additionally, the researchers predicted that participants would blame the adolescent under peer pressure less than the adolescent not under peer pressure, regardless of race. Finally, the researchers hypothesized that participants would blame the Black adolescent less than the White adolescent in the presence of peer pressure. As predicted, participants blamed the White adolescent more than the Black adolescent, regardless of peer pressure and blamed the adolescent less when under peer pressure, regardless of race. Additionally, in the peer pressure condition, the White adolescent was blamed more than the Black adolescent. There was no interaction between the adolescent’s race and peer pressure.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".