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Record W4221056654 · doi:10.1177/08969205221083503

<i>Capital</i> , Capitalism and Health

2022· article· en· W4221056654 on OpenAlexaff
Raju J Das

Bibliographic record

VenueCritical Sociology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork University
Fundersnot available
KeywordsCapitalismSurplus valueAppropriationCapital (architecture)Value (mathematics)SociologyPrecarityPrices of productionMeans of productionPower (physics)EconomicsPoliticsNeoclassical economicsFinancial capitalPolitical scienceHuman capitalEconomic growthLawGender studies

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has contributed to increased scholarly attention to an important ‘human need’: good health. This article is about the relation between workers’ health and capitalist production, as Marx examines it in his magnum opus . While Marx’s main focus in Capital Volume 1 is on the production of surplus value by workers and its appropriation by capitalists, he does provide insights into how capitalism ruins the health of workers themselves, although these insights are scattered. In this article, I systematically re-articulate and analyse Marx’s thoughts about workers’ health in relation to some of the key-categories of his political economy: the value of labour power relative to wages; employment precarity; long working day; hidden abode of production; capitalists’ despotic control over workers; and the capitalist transformation of nature. I briefly relate Marx’s ideas about workers’ health from Capital Volume 1 to some contemporary research on the social dimensions of health. I also show that Marx’s explicit ideas about workers’ health, which are my main focus, point to a broader approach to the topic that is only implicit in his thinking. I draw out some practical implications of this approach.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.470
Teacher spread0.378 · 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 designTheoretical or conceptual
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

Citations30
Published2022
Admission routes1
Has abstractyes

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