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Record W3027360624 · doi:10.5304/jafscd.2020.093.023

Is Canada's Supply Management System Able to Accommodate the Growth of Farm-direct Marketing? A Policy Analysis

2020· article· en· W3027360624 on OpenAlexaffabout
Patrick Mundler, Daniel‐Mercier Gouin, Sophie Laughrea, Simone Ubertino

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBusinessSupply managementAgricultureGovernment (linguistics)Supply chainSupply and demandProduction (economics)AgribusinessMarketingAgricultural economicsEconomics

Abstract

fetched live from OpenAlex

In recent years, Canada has witnessed a rapid growth in short food supply chains. As in other countries, such marketing channels have emerged in Canada in response to a growing demand among consumers for fresh, local products. However, a unique feature of Canadian agriculture is that dairy, egg, and poultry production are under supply management. The government requirement for producers in these sectors to purchase a quota ensures that output matches domestic demand. Until recently, though, little attention had been paid to how this system affects the development of short food supply chains in the country. The pur­pose of our study is to examine this emerging issue. The results of our policy analysis suggest that small farmers in Canada face multiple challenges when seek­ing to produce and market specialty products that are under supply management. Furthermore, the cost of entering supply-managed sectors for producers varies as each province is responsible for establishing its own quota exemption limits, mini­mum quotas, and new entrant programs. Our study indicates that supply management policies have important implications for local and regional food system development and for food diversity in Canada.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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 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

Citations11
Published2020
Admission routes2
Has abstractyes

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