Bibliographic record
Abstract
Sociologists have claimed that summer vacations widen inequality in academic achievement because, during the summer, students with access to learning opportunities at home continue to learn while students with limited access forget much of what they learned the previous school year. Part I of this dissertation argues that this claim is premised on the assumption that, summer after summer, the same lowest-achieving students fall further and further behind in all school subjects and the same highest-achieving students pull further and further ahead in all school subjects. I test the tenability of this assumption, which I term the assumption of within-student stability in learning across time and school subjects. I fit univariate-outcome and multivariate-outcome multilevel piecewise growth models to the math and reading achievement of 19,524 students in the United States between kindergarten and grade 5. Some of the results support the assumption of within-student stability, and others undermine it. I show that the growth models that have been informing theories of summer learning are sensitive to analytic choices. I offer avenues for advancing sociological theories of educational inequality and for improving the analytic methods that are used in research on educational inequality. In Canada and the United States, the provision of free summer learning programs is among primary strategies for improving student achievement and narrowing achievement gaps. However, research on their effectiveness is inconclusive. Part II of this dissertation argues that evaluations of summer learning programs can be strengthened by using analytic methods that are appropriate for the evaluation of time-varying treatments that may operate indirectly through time-varying characteristics of students. I fit regression-with-residuals models to the math and reading achievement of 2,111 students in Ontario, Canada, between grades 1 and 3. Results suggest that summer learning programs raise student achievement more when students attend a program in multiple summers rather than in a single summer. Results also suggest that the effects of summer learning programs vary significantly between students. However, estimates are noisy. I propose ways to increase capacity for rigorous evaluations of summer learning programs.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".