‘Similar, but different!’ - Cross-cultural analysis of the application of the CLASS Toddler in Quebec’s home-based childcares
Bibliographic record
Abstract
Analyse with a critical cross-cultural approach the application of the Classroom Assessment Scoring System, Toddler (CLASS-T) in Quebec’s home-based childcares (HBC). In ECEC, the quality of educator-child interactions has been identified as determining factor for children’s development and learning. Quality rating and improvement systems are increasingly assessing quality of interactions in \nHBC. Options are even outlined to do so with the CLASS – a standard-based tool to evaluate educator–child interactions in U.S. childcare center classrooms. However, using a standard-based tool out of his original context raises questions concerning its validity. The topic of applying the CLASS in HBC (especially outside the U.S.) has received marginal attention. Building upon Pastori and Pagani's (2017) study on the application of the CLASS in Italy, this study extend the critical cross-cultural approach to Quebec's HBC. Two focus groups of 2h, each including 10 HBC providers from Montreal (Quebec, Canada), were realized. Comparing interactions important for children in center-based and home-based childcares, they were questioned about elements of continuity, differences and disagreements and key-features of the educator–child interactions not captured by the CLASS-T tool. All providers were informed about the study and signed a consent form before the focus groups. At first, all providers agreed that the CLASS-T dimensions applied in HBC. Then, they talked about multiage group, organizational tasks, relationships with families. Such results bring some methodological and theoretical reflections to better capture the socio-educational reality of HBC. The results have implications for the assessment and improvement of quality of interactions offered in HBC.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 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".