School Climate, Teacher-Child Closeness, and Low-Income Children’s Academic Skills in Kindergarten
Bibliographic record
Abstract
In this study we used data on a sample of children in the Chicago Public Schools in areas of concentrated poverty-related disadvantage to examine associations between school climate and low-income children's language/literacy and math skills during the transition to kindergarten. We also explored whether teacher-child closeness moderated these associations. Multilevel modeling analyses conducted using a sample of 242 children nested in 102 elementary schools revealed that low adult support in the school was significantly associated with children's poorer language/literacy and math skills in kindergarten. Teacher-child closeness predicted children's higher language/literacy and math scores and moderated the association between low adult support and children's academic skills. Among children who were high on closeness with their teacher, those in schools with high levels of adult support showed stronger language/literacy and math skills. There were no significant associations between adult support and the academic skills of children with medium or low levels of teacher-child closeness. Results shed light on the importance of adult support at both school and classroom levels in promoting low-income children's academic skills during the transition to kindergarten.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".