Differential developmental courses of implicit and explicit biases for different other-race classes.
Bibliographic record
Abstract
= 200, age range from 4- to 19-year-olds) toward 2 different other-race groups that differ in terms of perceived social status (i.e., Whites and Blacks). At the youngest age, children showed both implicit anti-White and anti-Black bias at similar levels. However, these biases had different patterns of age-related change: implicit anti-Black bias remained strong and stable over time, whereas implicit anti-White bias declined after age 10. For explicit bias, children showed a decline in anti-Black and anti-White bias. Implicit and explicit biases were uncorrelated at all ages. The observed age-related changes demonstrate that it is possible for patterns of biases toward different races to diverge with age, and that perceived social status may contribute to the differential developmental patterns. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".