Introduction to the Section on Parenting in the Digital Age
Bibliographic record
Abstract
Abstract Amid the rapidly evolving digital age, caregivers are adapting their parenting strategies to effectively navigate and co-exist within this new landscape. Each chapter in this section on parenting in the digital age focuses on distinct facets and/or ages related to this overarching theme, with a collective goal of supporting a comprehensive understanding of this critical subject. The first chapter by Reich et al. provides an overview of definitions as well as various research approaches and methodologies focused on parenting and media. Following this, three chapters target specific developmental periods: early childhood (age 0–5; Hirsh-Pasek et al.), middle childhood (age 6–11; Bickham et al.), and adolescence (age 12–18; Wisniewski et al.). McDaniel et al.’s chapter reviews research on the role of digital technology in parent–child interactions, while the final chapter by Browne and colleagues describes how digital media can synergistically affect family interactions among all members (e.g., parents, siblings, etc.). Each chapter in this section shares current understandings, needs for future research, and practical strategies for families. Together, these chapters provide a multifaceted view of the extant research on parenting in the digital age and highlight key considerations for contemporary families.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".