How stable is program quality in child care centre classrooms?
Bibliographic record
Abstract
Abstract In the Early Childhood Education and Care (ECEC) sector there is a move to reduce oversight costs by reducing the frequency of quality assessments in providers who score highly consistently across time. However, virtually nothing is known about the stability of ECEC quality assessments over time. Using a validated measure of overall classroom quality, we examined stability of quality in a sample of over 1000 classrooms in licensed child care centres in Toronto, Canada over a 3-year period. Multilevel mixed-effects linear regression analyses revealed substantial instability across all types of ECEC centres, although publicly operated centres were somewhat more stable and tended to have higher quality scores. We also found substantial variance between classrooms within ECEC centres. None of the structural, child/family and neighbourhood characteristics we examined were significantly related to stability of quality ratings. The lack of stability found in our sample does not support the use of a risk-based approach to quality oversight in ECEC. Large within centre classroom quality variance suggest that all classrooms within a centre should be assessed individually. Furthermore, classroom level scores should be posted when scores are made public as part of accountability systems. Future research should, in addition to administrative data used in our study, explore how factors such as educator training, participation in program planning, reflective practices and ongoing learning might improve stability of quality over time.
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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".