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Record W3012602117 · doi:10.1177/0020731420913184

Health Inequities and the Shifting Paradigms of Food Security, Food Insecurity, and Food Sovereignty

2020· article· en· W3012602117 on OpenAlexaff
Arnel M. Borras, Faisal Ali Mohamed

Bibliographic record

VenueInternational Journal of Health Services · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsYork University
Fundersnot available
KeywordsFood sovereigntyFood securityMalnutritionArgument (complex analysis)PoliticsPolitical scienceFood systemsRight to foodEconomic growthSovereigntyDevelopment economicsSociologyEconomicsMedicineLawGeographyAgriculture

Abstract

fetched live from OpenAlex

Global hunger, food insecurity, and malnutrition are on the rise, partly resulting in furthering health inequities between classes and groups of peoples among and within countries. A systematic understanding of the links between inequities in food politics and health issues is a challenge, and it is partly complicated by the presence of 3 contending and shifting paradigms in food politics, namely, food security, food insecurity, and food sovereignty. These paradigms suggest competing views as to the causes of and solutions to hunger, food insecurity, and malnutrition. We argue that food sovereignty offers a better alternative for understanding and responding to food issues in relation to the challenge of tackling health inequities. However, the ways in which and the degree to which the issues of health inequities are incorporated in the current narratives and practices of food sovereignty is rather thin, and vice versa. Our concluding argument is that an interactive dialogue in research and social actions between food sovereignty, on the one hand, and health inequity, on the other hand, can mutually enrich and strengthen both fields of research and spheres of social actions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.134
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.410
Teacher spread0.302 · 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.

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

Citations51
Published2020
Admission routes1
Has abstractyes

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