A window into racial and socioeconomic status disparities in preschool disciplinary action using developmental methodology
Bibliographic record
Abstract
Abstract There are large differences in expulsions and suspensions on the basis of race starting in preschool and divergent explanations for their cause. The current study explores how developmental methodology can shed light on this vexing issue. We leverage two measures: (1) childcare provider complaints about children's behavior and their recommended disciplinary action (measured by parent report); and (2) observed disruptive behavior measured by a laboratory‐based standardized observation tool, the Disruptive Behavior Diagnostic Observation Schedule (DB‐DOS), among a large, sociodemographically diverse sample of children (n 430; mean age 4.79 years). We identified three latent class profiles on the basis of race/socioeconomic status (SES) and found disparities in childcare provider complaints based on profile membership. More specifically, children classified in the Black/Hispanic, poor and Black, nonpoor profiles both had significantly higher childcare provider complaints compared with children in the White/Hispanic, nonpoor profile. By contrast, there were no differences in observed disruptive behavior based on race/SES profiles. Finally, childcare provider complaints in preschool were associated with lower cognitive performance in elementary school, above and beyond observed disruptive behavior in preschool and race/SES profiles. Implications for classroom practice and contributions to the national debate on school disciplinary policies are discussed.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".