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Record W2676390937 · doi:10.71889/5fylantbak.29862392

Fostering The Local: Facilitating A Shift Away From A Global Agri-Food Industry

2017· article· en· W2676390937 on OpenAlexaboutno aff
Kathryn H. Howell

Bibliographic record

VenueNC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro) · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsFood systemsAgricultureNatural resource economicsOrder (exchange)Environmental degradationBusinessInequalityFood processingFood securityEconomicsPolitical scienceGeographyEcology

Abstract

fetched live from OpenAlex

The limited nature of the current agriculture system inevitably is unsustainable and is in desperate need of reform; there is a rising need to downscale and decentralize agricultural production methods in order to avoid impending future disasters that are inherent to a fossil fuel dependent system. This paper will examine the localized food economy as a potential alternative agricultural system and present the case studies of Waterloo, Canada and Tucson, Arizona. It will conclude with a proposal to facilitate a shift to a reflexively localized food economy in Raleigh, North Carolina. This paper will show the critical need to explore alternative systems that can reduce structural inequalities, environmental degradation and detrimental health effects caused by today’s dominant food system and how collaboration between farmers, researchers, community groups and local governments can facilitate an inclusive shift from the current global, productivist agricultural model to a sustainable, socially conscious and resilient local food economy.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0060.005
Open science0.0020.018
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.029
GPT teacher head0.224
Teacher spread0.195 · 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 designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueNC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro)→Same topicOrganic Food and Agriculture→French-language works237,207→