Selective social learning: New perspectives on learning from others.
Bibliographic record
Abstract
This special issue was motivated by the recent, wide-ranging interest in the development of children's selective social learning. Human beings have a far-reaching dependence on others for information, and the focus of this issue is on the processes by which children selectively and intelligently learn from others. It showcases some of the finest current work in this area and also aims to encourage new lines of investigation and new ways of thinking about how children learn from others. This issue also serves to highlight this new direction in basic research for the broader community of researchers, educators, and practitioners. Research on issues related to the facilitation of social learning has clear relevance to early educational contexts. In addition, by bringing together a varied pool of research on the same general topic, developmental scientists can discern the consistencies and themes that emerge from their collective efforts. The work presented here illustrates the breadth of children's selectivity across ages and domains of development, and it highlights the growing range of methods that can be recruited to investigate selectivity. This new research leads the field to reconsider the various ways in which social information guides learning and calls for novel theoretical accounts of these developments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".