Improving Education Outcomes for African American Youth: Issues for Consideration and Discussion
Bibliographic record
Abstract
The current state of low academic achievement among a large majority of African American students is complex. While the U.S. has long professed that a world-class education is the right of every child, there are still major inequities in the education system that leave African American children with fewer opportunities to receive a quality education throughout the educational pipeline (elementary, secondary, and postsecondary). African American students have fewer high-quality teachers, less resourced schools, fewer gifted programs, and limited access to college preparatory coursework. These inequities are further complicated by issues of poverty and geography. For African American students, reduced and constrained access to educational opportunities begins in the early years and persists throughout the PreK-12 education system and beyond. This report points out several points throughout the education pipeline where African American students are lost. Knowing these points of loss presents an opportunity to be strategic and deliberate with our investments in African American children and youth.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Report on inequities in the education pipeline for African American youth; the object is K-12 and postsecondary educational opportunity, not research practice.
The report addresses educational inequality among African American youth rather than research itself.
Report on PreK–12 educational inequities for African American youth; object is schooling, not research practice.
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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".