Children’s digital play as collective family resilience in the face of the pandemic
Bibliographic record
Abstract
In this article we explore how digital play as conducted through various social media and online meeting platforms facilitated resiliency and confidence building in children during the COVID-19 pandemic. Using day-in-the-life methodology and narrative inquiry, we disseminate and examine observations collected on children aged 2-10 during lockdown in a Newfoundland neighbourhood. Children utilized platforms such as TikTok, YouTube, and Zoom to embrace their agentic digital play in ways that repurposed the platforms to fulfil life milestones and social needs otherwise impacted and disrupted by pandemic restrictions. Through a series of vignettes and interviews, our research not only examines how such digital play benefits children and their healthy development, but how parents reacted to and assisted with their children's agentic digital platform manipulation and how this provided positive benefits and enriching experiences to the entire family. We additionally explore the conflicts and tensions both children and parents encountered in securely implementing free play via digital platforms, including fears of excess screen-time, digital dependency, and online threats, all of which risk limiting children's ability to independently explore their creativity and identities through digital play if not handled sensitively. Despite the hurdles to implementing digital play, this study exposes why it is essential for families to navigate this online terrain; this study ultimately poses that digital play and online platforms not only were beneficial to maintaining and building family resilience during the pandemic but will be vital assets in sustaining resiliency and positive mindsets moving forward with pandemic recovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".