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Record W4392935205 · doi:10.55016/ojs/jcph.v1i1.77900

Ventilator and the Vaccine

2024· article· en· W4392935205 on OpenAlexafffundabout
Deborah McPhail

Bibliographic record

VenueJournal of Critical Public Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Manitoba
FundersYork University
KeywordsBiopowerConceptualizationContext (archaeology)PandemicEugenicsNormativeSociologyCoronavirus disease 2019 (COVID-19)CriminologyPolitical scienceGerontologyMedicineDiseaseLawHistoryPoliticsPhilosophyPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

During the Covid-19 pandemic, the fat body was caught up in a complicated logics of life and death in the North American context, where “obesity” was regarded as an “underlying condition” for greater risk of severe disease and death from Covid. As such, bodies with high BMIs were refused ICU ventilator care in certain jurisdictions, which fat activists classified as eugenics. At the same time, some vaccination campaigns prioritized people with higher BMIs for scarcely available Covid vaccinations, also on the basis of fat bodies’ higher risk status for Covid death. This paper explores the seeming tension between two articulations of fat during the Covid pandemic, whereby fat bodies were simultaneously worthy of life and of death in the same moment. Using MBembe’s conceptualization of necropolitics, which draws out and expands upon Foucault’s notion of biopolitics, I argue that the two perspectives on fatness operated in tandem, within an overall temporal shift in classification of obesity; from that of a risk factor for eventual death to that of an emergent threat. Such a temporal shift, I argue, relied on well-worn eugenic patterns in Canada, through which “normative” white bodies were prioritized for life through a complex necropolitical practice by which fat bodies were both made live and let die.

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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.538
Teacher spread0.421 · 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 designNot applicable
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

Citations4
Published2024
Admission routes3
Has abstractyes

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