“The driver doesn't sit, he stands up like the Flintstones!”: Sibling Teaching During Teacher-Directed and Self-Guided Tasks
Bibliographic record
Abstract
Associations among sibling teaching strategies, learner behavior, age, age gap, gender, and social-cognitive skills (second-order false-belief and interpretive understanding of knowledge) were investigated in 63 sibling dyads in early and middle childhood. Two teaching tasks were introduced to the older sibling teacher: a teacher-directed task with unique subgoals (tractor construction) and a self-guided, repetitive task (tanagrams). Subsequently, the older sibling taught the younger sibling learner. Findings revealed effects for age and gender; older teachers employed a wider range of strategies, especially in the tractor task, and older learners were more successful and more involved. In the tractor task, teachers also used more instruction and encouragement with same-gendered siblings. Age-gap effects for teaching were evident in both tasks. Further, teachers employed more instruction, help, and demonstration strategies in the tractor task, whereas in the tanagrams task, they engaged in greater encouragement and verbal attention. Learner involvement in the two tasks was positively associated with teaching strategies reflecting guided participation but was negatively related to more controlling teaching strategies. Finally, the two social-cognitive measures were more strongly associated with teaching strategies in the teacher-directed tractor task. Findings are discussed in light of recent theory and research on sibling teaching and learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".