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Record W3167114782 · doi:10.3390/socsci10060210

“It’s Not Just about Work and Living Conditions”: The Underestimation of the COVID-19 Pandemic for Black Canadian Women

2021· article· en· W3167114782 on OpenAlexaffabout
Mélanie Knight, Renée Nichole Ferguson, Rai Reece

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPandemicRacismGender studiesNews mediaCoronavirus disease 2019 (COVID-19)SociologyWhite (mutation)Political scienceMedia studiesMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has increasingly been defined as the shecession for its disproportionate debilitating impact on women. Despite this gendered analysis, a number of health activists have called on governments to account for the experiences of Black communities as they are disproportionately suffering the effects of this pandemic. In the media’s address of the impact of the pandemic, we ask, what experiences are represented in news stories and are Black women present in these representations. Performing a content analysis of 108 news articles, a reading of media discourses through a racial lens reveals a homogenization of women’s experiences and an absence of the Black experience. In the small number of news stories that do focus on Black women, we see that the health disparities are not simply the result of precarious work and living conditions, but also the struggle against anti-Black racism on multiple fronts. In critiquing, however, we also bring forth the small number of news stories on the Black experience that speak to the desire and hope that can thrive outside of white supremacist structures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.236
GPT teacher head0.478
Teacher spread0.242 · 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.

Study designObservational
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

Citations15
Published2021
Admission routes2
Has abstractyes

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