Pilot testing a causal model that includes clusters of parent, child, teacher, and classroon variables, to explore the mechanisms underlying class size effects
Bibliographic record
Abstract
The purpose of this study was to explore the mechanisms underlying the effects of class size at the kindergarten level. The study began with the development of a systems-based, causal model that included clusters of parent, child, teacher, and classroom variables derived from the literature. Multiple Regressions were then used to test the 'fit' between the data collected for the study and the causal model. Data were provided by 9 kindergarten teachers from 16 classrooms in southern Ontario, 117 of their students (53.8% females), and their parents. Instruments used to collect data included teacher and parent questionnaires, the Early Childhood Environmental Rating Scale-Revised (Harms, Clifford, Cryer, 1998), Form L M of the Peabody Picture Vocabulary Test-Revised (Dunn Dunn, 1981), and the transcribed responses to the statement, "tell me about kindergarten from the time you arrive at school till the time you leave" (Pelletier, 1999). The results showed that there was a better fit between the data and the paths in the causal model flowing from the clusters of teacher and classroom variables as compared to the parent and child variables. Results also illustrated the pervasive nature of class size effects. Not only did class size explain some variance in a number of variables contained within the causal model as predicted, class size subsequently interacted with those same variables to modify other variables. This trajectory of interactions suggests that class size effects are cumulative. Moreover, class size effects were found to be modified by other variables in the model. Given the small sample size, findings from the study should be regarded as a preliminary test of the proposed model.
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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 | 0.028 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".