Implementation of Arkansas’s Initiative to Reduce Suspension and Expulsion of Young Children
Bibliographic record
Abstract
This article describes the development, initial implementation, and formative evaluation of Arkansas’s plan to reduce suspensions and expulsion from early care and education (ECE) settings. We describe how Arkansas used multifaceted implementation strategies to facilitate change in six areas: policy, workforce development, specialized supports, family partnerships, child screening, and data tracking. We also highlight key findings from the formative evaluation. For example, needs assessment data revealed that 40.8% of ECE providers suspended at least one child in the past year, and 9% expelled one or more children. We evaluated efforts to educate ECE providers on new nonexpulsion policies and new supports, and results indicate that 89.5% of directors agreed that they understand why young children should not be suspended or expelled, though the majority reported concern about implementing a nonexpulsion policy. Initial utilization data from a new ECE provider support system indicate that in the first quarter, 53 requests were submitted for help with challenging classroom behavior. Most requests involved male children over the age of 3, and one-third of the requests referenced traumatic events experienced by the children.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".