We got this: Evaluating racial socialization competency among diverse ethnic–racial groups.
Bibliographic record
Abstract
= 44.4 years) of youth between the ages of 10 and 18 recruited from across the United States. The sample was intentionally racially and ethnically diverse, with 26.1% identifying as Black, 24.2% identifying as Latinx, 24.9% identifying as Asian American, and 24.8% identifying as White. Confirmatory factor analyses replicated the previously identified structure of the RaSCS subscales, and scores were reliable. Multigroup measurement invariance analyses supported full scalar invariance across the four racial/ethnic subgroups for the Confidence, Skills, and General RS Stress subscales and partial scalar invariance for the Call to Action RS Stress subscale. These findings suggest that the RaSCS is an appropriate tool for assessing RS competency across racial and ethnic groups and that RS competency as a universal construct is relevant across groups. (PsycInfo Database Record (c) 2024 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".