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Record W2055361553 · doi:10.1111/1467-9353.00107

Vertical Linkages in Agri-Food Supply Chains: Changing Roles for Producers, Commodity Groups, and Government Policy

2002· article· en· W2055361553 on OpenAlexaff
Linda M. Young, Jill E. Hobbs

Bibliographic record

VenueReview of Agricultural Economics · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCommodityBusinessGovernment (linguistics)Supply chainFood supplyFood policyCommerceIndustrial organizationAgricultural economicsEconomicsFood securityAgricultureMarketingFinanceGeography

Abstract

fetched live from OpenAlex

Market developments arising from closer vertical linkages in agri-food supply chains have given rise to a variety of issues. This paper outlines key issues and discusses possible responses by producers, their associations, and government. A vision of the future organization of agricultural production serves as a basis for discussion. The continued increase in contracting between producers and processors is accompanied by issues of contract transparency, terms, negotiation, and dispute settlement. Other ramifications include producer access to supply chains and the decline of spot markets. Furthermore, the development of agricultural biotechnology products may force a rethinking of the rationale for public investment in agricultural research and development. Evaluation of market power needs to account for efficiency gains from nonstandard forms of organization to achieve a balanced appraisal of the public interest. Agricultural economists are urged to evaluate new forms of firm and industry structure on the basis of how they work in practice rather than in comparison to an ideal form.

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)0.009
metaresearch head score (Gemma)0.009
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 score0.011
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.013
Scholarly communication0.0110.012
Open science0.0010.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.203
Teacher spread0.185 · 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

Citations142
Published2002
Admission routes1
Has abstractyes

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