Assessment for Quality Improvement (AQI) Scores Among Early Childhood Education (ECE) in Toronto
Bibliographic record
Abstract
The Assessment for Quality Improvement (AQI) was developed as a quality assurance tool for early childhood education and care (ECEC) (Perlman et al., 2017; Toronto, n.d.a; Toronto, n.d.b). This validated tool is an observational measure that assesses classroom quality for infant, toddler, preschool, kindergarten, and school-age programs (Toronto, n.d.a). The main areas of focus include programming, the learning environment, and staff-child interactions (Toronto, n.d.b). This measure has been employed in ECEC programs throughout the City of Toronto to evaluate standards of care. The present study aims to explore variability in AQI scores across neighbourhoods, centres, and years in the City of Toronto. Additionally, neighbourhood-level characteristics and socioeconomic factors will be explored as predictors of AQI scores. References Perlman, M., Brunsek, A., Hepditch, A., Gray, K., & Falenchuck, O. (2017). Instrument development and validation of the infant and toddler Assessment for Quality Improvement. Early Education and Development, 28(1), 115–133. https://doi.org/10.1080/10409289.2016.1186468 Toronto. (n.d.a). AQI validation. https://www.toronto.ca/community-people/community-partners/early-learning-child-care-partners/assessment-for-quality-improvement-aqi/aqi-validation/ Toronto. (n.d.b). Quality ratings for child care centres. https://www.toronto.ca/community-people/community-partners/early-learning-child-care-partners/assessment-for-quality-improvement-aqi/
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".