Assessing structural and interaction quality in Quebec’s 4-year-old preschools and childcare centers to enhance quality : A comparative study
Bibliographic record
Abstract
This ongoing research seeks to measure educational quality in Quebec’s 4-year-old preschools and childcare centers (CC) and improve quality by identifying strengths and weaknesses. Research has shown that quality of children’s early experiences impacts their subsequent educational success (Heckman, 2011). However, researchers also found that only a handful of childcare services offer high quality experiences (Tayler et al., 2016). In Quebec, no study has measured both the quality of 4-year-old preschools and CC, two subsidized services accessible to a majority of children (Ertas & \nShields, 2012). Drawing upon a positivist normative approach of quality (Caublot et al., 2013), this study seeks to assess quality of these environments from two components: adult-child interaction quality and structural quality. This study is also based on the ecosystemic model of quality (Lemay, 2013), in which quality variables influence each other. The study will address this through correlational and regression analyses. The sample consists of 30 preschool classes and CC groups in the Montreal area. Structural variables will be measured using a questionnaire, while the CLASS Pre-K (Pianta et al., 2008) will be used to measure adult-child interaction quality. A letter describing the research will be sent to \nparticipants to obtain consent. A code assigned to each group and a password will ensure data confidentiality. Based on previous research, we expect higher quality in CC than in preschools. However, learning support might be higher for preschool teachers. Results will inform stakeholders on quality levels and suggest avenues to improve 4-year-old children’s educational experience in Quebec.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".