Names, Expectations and the Black-White Test Score Gap
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
This paper investigates the question of whether teachers treat children differentially on the basis of factors other than observed ability, and whether this differential treatment in turn translates into differences in student outcomes. I suggest that teachers may use a child's name as a signal of unobserved parental contributions to that child's education, and expect less from children with names that "sound" like they were given by uneducated parents. These names, empirically, are given most frequently by Blacks, but they are also given by White and Hispanic parents as well. I utilize a detailed dataset from a large Florida school district to directly test the hypothesis that teachers and school administrators expect less on average of children with names associated with low socioeconomic status, and these diminished expectations in turn lead to reduced student cognitive performance. Comparing pairs of siblings, I find that teachers tend to treat children differently depending on their names, and that these same patterns apparently translate into large differences in test scores.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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