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Record W2894913147 · doi:10.15353/cfs-rcea.v5i3.283

Governance recommendations from forty years of national food strategy development in Canada and beyond

2018· article· en· W2894913147 on OpenAlexaffvenueabout
Peter Andrée, Mary Coulas, Patricia Ballamingie

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorporate governanceGovernment (linguistics)Action planFood securityFood policyConversationPolitical scienceAction (physics)Public administrationStrengths and weaknessesFood systemsBusinessPublic relationsEconomicsSociologyManagementAgricultureGeography

Abstract

fetched live from OpenAlex

This paper contributes to Canada’s current national food policy discussion by introducing lessons gleaned from the development of two earlier Canadian government food policy efforts, A Food Strategy for Canada (1977) and Canada’s Action Plan for Food Security (1998), as well as lessons drawn from national food strategy development in seven other countries. By examining the strengths and weaknesses of these previous policy-making processes, we show how today’s food policy conversation builds on the legacy of 1998's Action Plan. We then offer food policy governance recommendations designed to avoid the mistakes of the previous efforts. This paper explores international precedents for governance mechanisms designed to be inclusive of key food systems’ stakeholders, and to meaningfully include multiple levels of government in food governance. Drawing on both our domestic and international research, we conclude by recommending the establishment of a multi-sectoral and inter-governmental National Food Policy Council. We show how such a Council, operating in close cooperation with other key mechanisms, could help govern the pan-Canadian food strategy we advocate.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.691

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 studies0.0000.000
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 head0.056
GPT teacher head0.269
Teacher spread0.213 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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