School readiness and later achievement: A French Canadian replication and extension.
Bibliographic record
Abstract
We first replicated the data analytic strategy used in Duncan et al. (2007) with a population-based data set of French-speaking children from Quebec (Canada). Prospective associations were examined between cognitive, attention, and socioemotional characteristics underlying kindergarten school readiness and second grade math, reading, and general achievement. We then extended this school readiness model by including motor skills as an additional element in the prediction equation and expanded the original strategy by including classroom engagement. The Montreal Longitudinal-Experimental Preschool Study, featured in Duncan et al., served as the Canadian reference group. In the replication model, kindergarten cognitive and attention characteristics predicted achievement by the end of 2nd grade. Although inconsistent across outcomes, behavioral problems and skills also emerged as predictors of some aspects of later achievement. Coefficients for kindergarten math skills were largest, followed by attention skills, receptive language skills, attention problems, and behavior. Most coefficients resembled those generated in the initial study. In our extension model, fine motor skills added their significant contribution to the prediction of later achievement above and beyond the original key elements of school readiness. Our extension model confirmed prospectively associations between kindergarten cognitive, attention, fine motor, and physical aggression characteristics and later achievement and classroom engagement by the end of 2nd grade. Although they comparatively showed better long-term benefits from stronger early attention skills, girls with less kindergarten cognitive skills were more vulnerable than boys with similar deficits when predicting 2nd grade math.
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.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".