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Record W4220889253 · doi:10.1177/0192513x221079328

Whiteness in the COVID-19 Pandemic: Who is Talking About Racism With Their Kids?

2022· article· en· W4220889253 on OpenAlexaffabout
Keira B. Leneman, Sydney Levasseur-Puhach, Sarah Gillespie, Irlanda Gomez, Gordon C. Nagayama Hall, Leslie E. Roos

Bibliographic record

VenueJournal of Family Issues · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
Fundersnot available
KeywordsSocializationRacismSocioeconomic statusPandemicPsychologyCoronavirus disease 2019 (COVID-19)White (mutation)Developmental psychologyDemographyMedicineSociologyPopulationGender studies

Abstract

fetched live from OpenAlex

The present study investigated factors associated with parent awareness and socialization surrounding COVID-19-related racial disparities among White parents of children ages 1.5–8 living in Canada and the United States ( N = 423, 88% mothers). Participants responded to an online survey about parenting during the pandemic between mid to late-April 2020. Participants reported on their level of awareness of COVID-19-related racial disparities as well as how often they discussed these with their children. Although 52% reported some level of awareness, only 34% reported any amount of discussion with their child about it. Regression models were used to further examine stress-related, socioeconomic, parenting, and news-watching associations with awareness and socialization. This study provides unique insight into which White parents are aware of racial inequities exposed by the pandemic and which are choosing to speak to their children about them. Current summary recommendations for White racial socialization and related research are also presented.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.144
GPT teacher head0.435
Teacher spread0.291 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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