Who’s Got Talent for Identifying Talent? Predictors of Equitable Gifted Identification for Black and Hispanic Students
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
Students who are Black or Hispanic have long been disproportionately represented in K–12 gifted and talented services. However, there are schools that have diverged from this trend by identifying atypically high numbers of Black and Hispanic students. In this conceptual replication of Peters and Johnson, we present predictors of whether a school offers gifted services (i.e., access) and representation indices for Black and Hispanic students (i.e., equity) within schools that enroll 10 or more Black or Hispanic students. Our results show that state policy mandates for gifted education are predictive of higher levels of access to and equity within gifted services for these schools. The average achievement and socioeconomic status of the district were positive predictors of access and equity while the district proportion eligible for special education services was a negative predictor of both. Finally, we end with a description of how the top 5% most-equitable schools in the country look different from their peers.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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