Social Construction Is Racial Construction: Examining the Target Populations in School-Choice Policies
Bibliographic record
Abstract
Purpose: We examine policy influencers' perceptions of the targets of school-choice policy across five states, exploring how constructions varied for White and racially minoritized families, whether policy actors conceived of the "target" of policy as the child or the parent, and how these racialized constructions varied across different types of school-choice policies. Research Methods/Approach: We conducted 56 semistructured interviews in 2019 with state-level stakeholders across five states. Findings: We found that policy actors generally viewed White families as strong and racially minoritized families as weak. However, for both groups, we found variation in whether these constructions were positive or negative and differences between students and parents. We find that social constructions are fluid, with varying, sometimes conflicting and contradictory views of racially minoritized and White parents in the same period, within the same state context. Despite the salience of race throughout social constructions of the target population, policy actors primarily used color-evasive references. In general, we found little variation in policy components at the state level. Implications: Our work demonstrates how racialized social constructions matter for equity in school-choice policy, with implications for local, state, and federal policy and for future research.
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How this classification was reachedexpand
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".