The Case for an Intersectional Approach to Trauma-Informed Practices in K–12 Schools for Black Girls
Bibliographic record
Abstract
Abstract Black girls are the only group of girls across the United States disproportionally suspended from school. Studies have documented that disproportionality cannot be explained solely by greater misbehavior among students of color. Instead, discipline disparities are also informed by punitive/inequitable discipline policies and practices, less discussed has been the relationship between childhood adversity and school discipline outcomes at the intersection of race and gender. Examining this phenomenon is important and timely as schools are increasingly providing trauma-informed practices to support socioemotional learning. Yet doing so without data-driven practices rooted in an understanding of disproportionate adversity may render these practices insufficient for Black girls. Thus, this study asks, what types of childhood adversities do Black girls have the greatest risk of experiencing? Using 2016–2019 data from the National Survey of Children’s Health (N = 63,674), risk ratios and Pearson’s chi-square test of independence were performed to determine across-race and within-gender group differences by the type of childhood adversity. Analyses demonstrated that Black girls had a greater risk for six out of nine adversities compared with other girls of color and seven out of nine compared with White girls.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".