Bibliographic record
Abstract
The purpose of this study was to understand typically developing children’s repetitive behavior in a free-play, daycare setting. By studying repetition in a non-Montessori setting, we tested the assumption that repetition is a characteristic behavior of all young children and not limited to the Montessori environment. Although Maria Montessori identified repetition during her observations, there is little empirical evidence to support her claim: most research has considered repetition in terms of psychopathology. We collected naturalistic observational data on 31 3- to 6-year-old children for a total of 101 hours to investigate the frequency, contexts, and structure of repetitive bouts. Multilevel model results suggest the ubiquity of repetition, as all children in the study engaged in motor repetition. Furthermore, repetition occurred throughout all free-play activities (construction, animation, fantasy play, rough-and-tumble play, and undirected activity), although repetition was not equally distributed across activities. Motor repetition was not equal across ages either; younger children engaged in more motor repetition than did older children. To understand the structure of repetition, our study also looked at the length of repetition bouts, which ranged from 2 to 19 repetitions and averaged 2.86 repetitions per bout. This natural history of repetition is an influential starting point for understanding the role of repetition in development and is informative to both Montessori and non-Montessori early childhood educators.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".