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Record W4405435212 · doi:10.1111/chso.12931

Family Media Practices in a Post‐Pandemic Future: Conversations From a Transglobal Research Project

2024· article· en· W4405435212 on OpenAlexafffundabout
Natalie Coulter, Diana Carolina García Gómez, Sarah Healy, Hyeon‐Seon Jeong, Maureen Mauk, Lindsay C. Sheppard, Rebekah Willett, Xinyu Zhao

Bibliographic record

VenueChildren & Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsYork University
FundersNational Research FoundationNational Research Foundation of KoreaYork UniversityUniversity of Wisconsin-Madison
KeywordsPandemicSociologyCoronavirus disease 2019 (COVID-19)Political sciencePsychologyMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.432
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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