Imitation as a Learning Strategy during Sibling Teaching
Bibliographic record
Abstract
The sibling relationship represents a unique bond characterized by a high degree of closeness and intimacy, which fosters teaching and learning. Two studies investigated associations between sibling-directed teaching, imitation as a learning strategy, and learner involvement during a semi-structured, video-taped construction task. Study 1 also examined associations with the teacher’s Theory of Mind (ToM) abilities; Study 2 focused on associations with birth order and sibling relationship quality. In both studies, siblings ranged from preschool-age to the cusp of middle childhood (Study 1 n = 61; Study 2 n = 72). Findings across both studies indicated that learners engaged in significantly more nonverbal than verbal imitation and imitation was predominately immediate and not deferred. Teachers responded to both verbal and nonverbal learner imitation positively, corrected, or did not respond. In Study 1, learner task involvement was predicted by learner age and nonverbal imitation, while teaching strategies that were positively related to the teacher’s ToM abilities were associated with learner imitation. In Study 2, younger siblings’ reports of a positive sibling relationship were significantly associated with learner imitation. Birth order differences were only evident for younger (but not older) sibling learner imitation and task involvement. Findings are discussed in light of relationships and social constructivist theories of development.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".