School Readiness of Children of Immigrants: Does Parental Involvement Play a Role?<sup>*</sup>
Bibliographic record
Abstract
Objectives. Using data from the Early Childhood Longitudinal Survey—Kindergarten Cohort, this article analyzes the link between parental involvement and the school readiness of children of immigrants. Methods. Multivariate regression models estimate the association between parental involvement and the school readiness in English proficiency and math scores of children of immigrants. They also estimate the impact of this association on the gap in math scores between children of immigrants and children of natives. Results. Results demonstrate that parental involvement is associated with an increase in the level of English proficiency for children of immigrants. Parental involvement also is associated with a decrease in the gap in math scores between immigrant children from English‐ and non‐English‐speaking backgrounds. Parental involvement decreases the gap in math scores between children of immigrants and children of the native born by a third of a standard deviation. Conclusion. Given that parental involvement appears to benefit children of immigrants and given that they have lower academic achievement than children of the native born, these findings suggest that parental involvement policies and practices targeting children of immigrants could help decrease the academic achievement gap between children of immigrants and children of the native born.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".