Digital Media Use and Language Development in Early Childhood
Bibliographic record
Abstract
Abstract The pervasiveness of digital media and the importance of language skills underscore a pressing need to understand the role of media in language development. Historically, research has focused on the quantity of children’s media use but there has been a recent focus on content (i.e., the kinds of media that children use), context (i.e., who children use media with), and interactivity (i.e., whether children watch content passively or can respond contingently) as well as technoference (i.e., adult technology use around children). Meta-analyses suggest that while associations between media quantity and early language skills are either negative or null, both educational content (intending to convey knowledge) and co-use (use with others) are often associated with stronger skills, whereas adult technology use can disrupt parent–child interactions. Some researchers have also argued that interactive media that can respond contingently to children may support language, although more research is needed. Here, we focus on these aspects of media use and their associations with children’s language development in early childhood (0–6 years). We review the current literature, propose important directions for future research, and provide recommendations for researchers in this area and for various stakeholder groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".