Family Media Practices in a Post‐Pandemic Future: Conversations From a Transglobal Research Project
Bibliographic record
Abstract
ABSTRACT This article is co‐written by a team of researchers who worked together during the pandemic to conduct parallel research projects in their home countries, collectively referencing the project as Children, Media and Pandemic Parenting. Our article consists of a series of curated thought pieces, drawing on interviews with parents in Australia, Canada, China, Colombia, South Korea and the United States. The pieces consider how family media practices gained greater degrees of nuance during the pandemic through an examination of four interlinked themes: Screen time, creativity, schooling and regulation. We discuss how the intensified presence of digital technologies in domestic life was accompanied by an intensified sense of parental responsibility, creating undue pressure to make the ‘right’ decisions while often feeling ill‐equipped to do so. We argue that parents could be better supported to make considered choices about media practices in the home if responsibilities were more widely distributed to include the likes of cultural institutions and, where appropriate (e.g., in the instance of tech companies), regulated by Government. Ideally, this support would be accompanied by the development of a range of tools that are responsive to the complex and evolving needs of 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".