Who’s Got Talent for Identifying Talent?: Predictors of Equitable Gifted Identification for Black and Hispanic Students
Bibliographic record
Abstract
Students who are Black or Hispanic have long been disproportionately underrepresented 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 paper we present predictors of access to and equity within gifted and talented populations for schools that enroll ten 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 at the school level. The average achievement and socio-economic status of the district were positive predictors of access and equity while 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.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".