Portrait of the preschool educational trajectories of Montréal children. Results of the Montréal Survey on the Preschool Experiences of Children Kindergarten (MSPECK).
Bibliographic record
Abstract
The role of educational services in early childhood development and in the reduction of social health inequalities is widely recognized (McCain et al., 2011;Vandenbroeck & Lazzari, 2014).For James Heckman, recipient of the Nobel Prize in Economics, investing in quality educational services provides the best return and makes a substantial difference in children's lives by facilitating their integration into the school system and society (Heckman, 2006).In Québec, there is not just one preschool educational pathway: some children stay at home and some attend daycare or four-year-old kindergarten; for others, it is a combination of both.Therefore, when they start school, children may have had diverse experiences.This report uses data from the Montréal Survey on the Preschool Experiences of Children in Kindergarten (MSPECK) to outline the educational trajectories of Montréal children from birth to the beginning of kindergarten.A short description of the survey methodology is followed by the results, presented in three sections: The first section looks attendance in different types of childcare services.The second focuses more specifically on attendance in educational services, including public four-year-old kindergarten.The third section describes children's exposure to other educational activities, at home and in the community.Concerned by the effects of social inequalities in health on children's development, we draw attention, as we describe the results, to differences observed between the situations of children from low-income families and those from more affluent ones.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".