Validation of the French version of the classroom assessment scoring system infant and toddler in Quebec
Bibliographic record
Abstract
The objective of this study is to validate the French version of the Classroom Assessment Scoring System (CLASS) infant and toddler, as employed to assess the quality of interactions in groups of children under 3 years old, in childcare centers in Quebec where French is the official language. Indeed, when using a different language version of a standard-based tool outside its original context, an important step is to verify that it remains reliable and valid for measuring the research construct. This validation study was conducted in Montreal area (Quebec, Canada). The subjects were 154 classrooms (46 infant, 108 toddler) located within a representative sample of 68 childcare centers. Live classroom observations were conducted in the fall 2018 with the CLASS and other measures of process quality. Results replicate the factor structures of the original versions of the CLASS tool and provide evidence for the good reliability (inter-rater reliability, internal consistency) and validity (criterion and construct) of the French versions. The discussion highlights cross-cultural differences in the classrooms, childcare centers, and regulations that could explain some differences obtained in this research and, therefore, needs to be considered when using the CLASS in French to have a reliable and valid tool to measure the quality of interactions.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".