Implications of video chat use for young children's learning and social–emotional development: Learning words, taking turns, and fostering familial relationships
Bibliographic record
Abstract
Parents of young children use video chat differently than other screen media, paralleling expert recommendations (e.g., American Academy of Pediatrics Council on Communications and Media, 2016), which suggest that video chat, unlike other screen media, is acceptable for use by children under 18 months. Video chat is unique among screen media in that it permits contingent (time-sensitive and content-sensitive) social interactions. Contingent social interactions take place between a child and a partner (dyadic), with objects (triadic), and with multiple others (multi-party configurations), which critically underpin development in multiple domains. First, we review how contingent social interaction may underlie video chat's advantages in two domains: for learning (specifically learning new words) and for social-emotional development (specifically taking turns and fostering familial relationships). Second, we describe constraints on video chat use and how using chat with an active adult (co-viewing) may mitigate some of its limitations. Finally, we suggest future research directions that will clarify the potential advantages and impediments to the use of video chat by young children. This article is categorized under: Linguistics > Language Acquisition Psychology > Learning Cognitive Biology > Social 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".