School Starting Age and Cognitive Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We present evidence of a positive relationship between school starting age and children's cognitive development from ages 6 to 18 using a fuzzy regression discontinuity design and large‐scale population‐level birth and school data from the state of Florida. We estimate effects of being old for grade (being born in September vs. August) that are remarkably stable—always around 0.2 SD difference in test scores—across a wide range of heterogeneous groups, based on maternal education, poverty at birth, race/ethnicity, birth weight, gestational age, and school quality. While the September‐August difference in kindergarten readiness is dramatically different by subgroup, by the time students take their first exams, the heterogeneity in estimated effects on test scores effectively disappears. We do, however, find significant heterogeneity in other outcome measures such as disability status and middle and high school course selections. We also document substantial variation in compensatory behaviors targeted towards young‐for‐grade children. While the more affluent families tend to redshirt their children, young‐for‐grade children from less affluent families are more likely to be retained in grades prior to testing. School district practices regarding retention and redshirting are correlated with improved outcomes for the groups less likely to use those remediation approaches (i.e., retention in the case of more affluent families and redshirting in the case of less affluent families.) Finally, we find that very few school policies or practices mitigate the test score advantage of September‐born children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 it