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Record W2111410126 · doi:10.1186/1747-5341-8-16

Access to nutritious food, socioeconomic individualism and public health ethics in the USA: a common good approach

2013· article· en· W2111410126 on OpenAlexaff
Jacquineau Azétsop, Tisha Joy

Bibliographic record

VenuePhilosophy Ethics and Humanities in Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsWestern University
Fundersnot available
KeywordsCommodityFood securityMindsetCommoditizationFood systemsSustainabilityEmpowermentMarketingBusinessPublic economicsEconomicsEconomic growthAgricultureMarket economyGeography

Abstract

fetched live from OpenAlex

Good nutrition plays an important role in the optimal growth, development, health and well-being of individuals in all stages of life. Healthy eating can reduce the risk of chronic diseases, such as heart disease, stroke, diabetes and some types of cancer. However, the capitalist mindset that shapes the food environment has led to the commoditization of food. Food is not just a marketable commodity like any other commodity. Food is different from other commodities on the market in that it is explicitly and intrinsically linked to our human existence. While possessing another commodity allows for social benefits, food ensures survival. Millions of people in United States of America are either malnourished or food insecure. The purpose of this paper is to present a critique of the current food system using four meanings of the common good--as a framework, rhetorical device, ethical concept and practical tool for social justice. The first section of this paper provides a general overview of the notion of the common good. The second section outlines how each of the four meanings of the common good helps us understand public practices, social policies and market values that shape the distal causal factors of nutritious food inaccessibility. We then outline policy and empowerment initiatives for nutritious food access.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.492
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.007
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 head0.669
GPT teacher head0.522
Teacher spread0.147 · 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

Citations61
Published2013
Admission routes1
Has abstractyes

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