The school entry gap: Socioeconomic, family, and health factors associated with children’s school readiness to learn. Early Education and Development 18
Bibliographic record
Abstract
Notwithstanding the constant debate in the scientific and policy literature on the pre-cise meaning of school readiness, research consistently demonstrates a wide varia-tion between groups of children resulting in a gap at school entry. Recently, the teacher-completed Early Development Instrument (EDI), a new measure of chil-dren’s school readiness in 5 developmental areas, was developed, tested, and imple-mented in Canada. EDI results confirmed the existence of a school entry gap. In this article, we explore factors in 5 areas of risk: socioeconomic status, family structure, child health, parent health, and parent involvement in literacy development. In a se-ries of logistic regressions, we demonstrate that variables in all 5 areas, as well as age and gender, contribute to the gap. Child’s suboptimal health, male gender, and com-ing from a family with low income contribute most strongly to the vulnerability at school entry. As the purpose of a tool like the EDI is primarily to assist in popula-tion-level reporting on children’s school readiness, the results of our study provide additional and much-needed evidence on the instrument’s sensitivity at the individ-ual level, thus paving the way for its use in interpreting children’s school readiness in the context of their lives and the communities in which they live.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".