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Record W3098386305 · doi:10.1177/0956247820970094

COVID-19 responses: infrastructure inequality and privileged capacity to transform everyday life in South Africa

2020· article· en· W3098386305 on OpenAlexaff
Jiska de Groot, Charlotte Lemanski

Bibliographic record

VenueEnvironment and Urbanization · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsOvercrowdingSocial distanceEveryday lifeEconomic growthPovertyInequalityDevelopment economicsDistancingCoronavirus disease 2019 (COVID-19)Public healthPandemicPolitical scienceSocial isolationSociologyPsychologyEconomicsMedicine

Abstract

fetched live from OpenAlex

Throughout the early months of 2020, COVID-19 rapidly changed how the world functioned, with the closure of borders, schools and workplaces, national lockdowns, and the rapid normalization of “self-isolation” and “social distancing”. However, while public health recommendations were broadly universal, human capacity to accordingly transform everyday life has differed significantly. We use the example of South Africa to highlight the privileged nature of the ability to transform one’s life in response to COVID-19, arguing that the virus both highlights and exacerbates existing inequalities in access to infrastructure. For those living in urban poverty in South Africa, where access to basic infrastructure is limited, and where overcrowding and high density are the norm, it is frequently impossible to transform daily life in the required ways. The failure of global public health recommendations to recognize these inequalities, and to adapt advice to national and local contexts, reveals significant limitations that extend beyond this specific global pandemic.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0040.005
Open science0.0010.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.044
GPT teacher head0.274
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations81
Published2020
Admission routes1
Has abstractyes

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