The Case for an Intersectional Approach to Trauma-Informed Practices in K–12 Schools for Black Girls
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
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.
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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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