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Disruptive Food Supply Chains

2018· reference-entry· en· W2896713784 on OpenAlexaboutno aff
Tony Beck

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsFood systemsStatus quoEquity (law)Social equalityFood sovereigntySustainable agricultureEnvironmental stewardshipMainstreamBusinessPublic economicsEconomicsAgricultureEconomic growthPolitical scienceFood securityEnvironmental resource managementMarket economyGeography

Abstract

fetched live from OpenAlex

Alternative food movements have, from their origins, espoused values of social justice and environmental stewardship in an attempt to challenge existing economic and social norms related to food and farming. Three alternative food movements in North America exemplify the trade-offs between the three pillars of sustainable development: social equity, environment, and economy. Organic food has brought environmental benefits, but has struggled to challenge the status quo and promote the social benefits of the original movement when it goes to scale. Farmers’ markets have brought social and environmental benefits, but only in some cases reduced costs when compared to mainstream market levels. Consequently, good-quality food is often out of reach of low-income groups, as highlighted in a case study of access by underserved people in British Columbia, Canada. Regional food movements are a hybrid approach that balance some of the gains and some of the challenges of these systems. The extraordinary concentration of power in North American food systems stands in contrast to notions of social equity and undermines efforts to effect change in pursuit of sustainable alternative food systems.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0070.008
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.020
GPT teacher head0.213
Teacher spread0.193 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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