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Record W2773188385 · doi:10.1177/0020731417742258

Can Neoliberal Capitalism Affect Human Evolution?

2017· article· en· W2773188385 on OpenAlexaff
Robert Chernomas, Ian Hudson, Gregory Chernomas

Bibliographic record

VenueInternational Journal of Health Services · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsCapitalismInequalityAffect (linguistics)Socioeconomic statusResource (disambiguation)Transgenerational epigeneticsEconomic growthDevelopment economicsPolitical scienceSociologyEconomicsPopulationBiologyPoliticsLaw

Abstract

fetched live from OpenAlex

The connection between genes and health outcomes is significantly moderated by social factors. Health inequalities result from the differential accumulation of exposures and resource access rooted in class-based circumstances. In the neoliberal era in the United States, changed physical and socioeconomic conditions facing the poorer members of society have been characterized as traumatogenic (capable of producing a wound or injury). This paper will argue that research that points to the transgenerational influence of environmental impacts on health suggests 2 important reconsiderations of the link between the economy and health. First, an understanding of the health of any society requires an understanding not only of current but also past environmental conditions and the economy that produces those conditions. Second, it suggests that the way in which economic policy is analyzed needs to be reconsidered to incorporate the transgenerational impacts of environmental conditions produced by those policies.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.417
Teacher spread0.390 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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