The School Entry Gap: Socioeconomic, Family, and Health Factors Associated With Children's School Readiness to Learn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Notwithstanding the constant debate in the scientific and policy literature on the precise meaning of school readiness, research consistently demonstrates a wide variation between groups of children resulting in a gap at school entry. Recently, the teacher-completed Early Development Instrument (EDI), a new measure of children's school readiness in 5 developmental areas, was developed, tested, and implemented 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 series 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 coming 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 population-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 individual 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it