School readiness and later achievement: Replication and extension using a nationwide Canadian survey.
Bibliographic record
Abstract
In this article we replicate and extend findings from Duncan et al. (2007). The 1st study used Canada-wide data on 1,521 children from the National Longitudinal Survey of Children and Youth (NLSCY) to examine the influence of kindergarten literacy and math skills, mother-reported attention, and mother-reported socioemotional behaviors on 3rd-grade math and reading outcomes. Similar to Duncan et al., (a) math skills were the strongest predictor of later achievement, (b) literacy and attention skills predicted later achievement, and (c) socioemotional behaviors did not significantly predict later school achievement. As part of extending the findings, we incorporated a multiple imputation approach to handle missing predictor variable data. Results paralleled those from the original study in that kindergarten math skills and Peabody Picture Vocabulary Test-Revised scores continued to predict later achievement. However, we also found that kindergarten socioemotional behaviors, specifically hyperactivity/impulsivity, prosocial behavior, and anxiety/depression, were significant predictors of 3rd-grade math and reading. In the 2nd study, we used data from the NLSCY and the Montreal Longitudinal-Experimental Preschool Study (MLEPS), which was included in Duncan et al., to extend previous findings by examining the influence of kindergarten achievement, attention, and socioemotional behaviors on 3rd-grade socioemotional outcomes. Both NLSCY and MLEPS findings indicated that kindergarten math significantly predicted socioemotional behaviors. There were also a number of significant relationships between early and later socioemotional behaviors. Findings support the importance of socioemotional behaviors both as predictors of later school success and as indicators of school success.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".