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Record W2894867036 · doi:10.15353/cfs-rcea.v5i3.281

Federalism and fragmentation: Addressing the possibilities of a food policy for Canada

2018· article· en· W2894867036 on OpenAlexaffvenueabout
Sarah Berger Richardson, Nadia Lambek

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsFederalismJurisdictionCorporate governanceExclusive jurisdictionFood policyGovernment (linguistics)Cooperative federalismFood systemsPublic administrationFederal jurisdictionPolitical scienceBusinessFood securityAgricultureLawPoliticsGeography

Abstract

fetched live from OpenAlex

Canadian federalism poses unique challenges for the development of a national food policy. Under the Constitution Act, 1867, the federal government and the provinces are granted powers to govern exclusively in certain areas and to share jurisdiction in others. Where one level of government has exclusive jurisdiction, the other level of government is not permitted to interfere. However, good food system governance requires addressing policy coherence and coordination horizontally, across sectors such as agriculture, trade, health, finance, environment, immigration, fisheries, social protection, and vertically between the federal government, the provinces, and international and transnational actors. The development of a national food policy for Canada offers an opportunity to harmonize law and policymaking, and clarify the key roles that all levels of government play in the development and governance of food systems. This will require identifying sites of conflict and overlap, but also spaces for collaboration, coordination, and innovation. A national food policy will necessarily have to work within the constraints of Canadian constitutional law, but federalism and the division of powers can be harnessed to create a more just, equitable, democratic and sustainable food system.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.058
GPT teacher head0.246
Teacher spread0.188 · 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 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

Citations10
Published2018
Admission routes3
Has abstractyes

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