Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data
Bibliographic record
Abstract
the University of Toronto. Opinions reflect those of the authors and do not necessarily reflect those of the granting agencies. The authors benefited from the ideas of Susan Murphy, Ben Hansen, Natalya Verbitsky, and participants in the hierarchical linear modeling class at the University of Michigan in Fall Term 2003. We appreciate the extremely helpful feedback from the Editor, the Associate Editor, and the two anonymous reviewers. Evaluating Kindergarten Retention Policy 1 This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable-unit-treatment-value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. However, SUTVA is questionable because a child’s potential outcomes will plausibly depend on which school that child attends and also on treatment assignments of other children. We develop a causal model that allows school
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".