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Record W2164582803 · doi:10.1080/1523908032000121175

Transformations in Food Consumption and Production Systems

2003· article· en· W2164582803 on OpenAlexfundno aff
Ken Green, Mark Harvey, Andrew McMeekin

Bibliographic record

VenueJournal of Environmental Policy & Planning · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsSustainabilityConsumption (sociology)Production (economics)AgricultureContext (archaeology)Variety (cybernetics)Food systemsSustainable agricultureFood processingNatural resource economicsBusinessSustainable consumptionEconomicsEnvironmental economicsEconomic systemAgricultural economicsFood securityPolitical scienceGeographySocial scienceMicroeconomicsSociologyComputer scienceEcology

Abstract

fetched live from OpenAlex

The sustainability of global food consumption and production systems (FCPSs) over the next 25 years depends on changing economic developments, changing household consumption patterns and new technological developments, as well as on the environmental context of agriculture. This paper explores the interaction of these dynamics by examining the claims for sustainability of supposedly competing 'strategies' for the transformations of FCPSs. An FCPS includes not just agricultural production but also processing, retailing, eating and waste disposal phases. The four strategies are characterized as 'industrialized', 'traditional sustainable', 'organic' and 'new industrialized'. The paper argues that each strategy works in a variety of politico-economic structures and that focusing only on food crop production (such as in agriculture) ignores major environmental problems that are due to other phases of a food's lifecycle.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.012
Scholarly communication0.0060.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.210
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 designObservational
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

Citations27
Published2003
Admission routes1
Has abstractyes

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