Investigating relationships between screen time and young children's social emotional development
Bibliographic record
Abstract
In this project, I investigate the extant literature examining relationships between young children’s (birth to 6 years old) screen time use and their social emotional development. The social and emotional learning experiences that children have help them to build the foundation for developing various social and emotional skills. Children’s play is considered a significant component in supporting and promoting their social emotional development and, with the infiltration of screen-based technology in our society, children are deprived from opportunities for play. In exploring this topic I examined the benefits of play, the effects of play deprivation, and the possible drawbacks as well as the possible benefits of children’s screen time use on their social and emotional development. To connect the literature reviewed to practice in early childhood education, I have prepared a presentation for parents to support them in promoting their children’s social and emotional development while recognizing the role that screen-based devices has in their lives. Through my exploration of the literature on this topic I learned that while there may be some social emotional benefits from screen time, screen time is not a replacement for the extraordinary benefits that children gain from social play. Recommendations for future study include learning more about the long-term effects of children’s screen time use on their social and emotional development. Recommendations for future practice include the need for parents and educators to be active participants in their children’s screen time use, in addition to being role models in their own screen time habits.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".