The Impact of Junior Kindergarten on Math Skills in Elementary School
Bibliographic record
Abstract
Using data from the first cycle of the National Longitudinal Survey of Children and Youth, this study examines the impact of junior kindergarten on children's later skills in math, above and beyond regional differences and individual/ household factors. It was hypothesized that earlier schooling would better prepare children for first formal learning of arithmetic, resulting in a more developed knowledge-base for optimal performance at the middle and end of elementary school. We also hypothesized that junior kindergarten attendance would reduce the performance gap between children from economically disadvantaged and those from advantaged families. Our results suggest that earlier schooling did not provide the cognitive boost that would lead to better performance in arithmetic, especially for girls. These results are above and beyond a number of controls (sex, age, region, SES, family functioning, family configuration, education, and family size). Junior kindergarten attendance did not help bridge the performance gap based on economic advantage/disadvantage. Primary school children from poor families who attended junior kindergarten did not perform on a par with their middle-class peers. This study makes an important case for enrichment in the central conceptual structures related to intuitive and informal mathematical knowledge through games and activities potentially featured in the junior kindergarten curriculum.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".