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Record W2894650455 · doi:10.15353/cfs-rcea.v5i3.275

Settler colonialism and the (im)possibilities of a national food policy

2018· article· en· W2894650455 on OpenAlexaffvenueabout
Sarah Rotz, Lauren Kepkiewicz

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of TorontoUniversity of Guelph
Fundersnot available
KeywordsFood systemsGovernment (linguistics)IndigenousScope (computer science)Public policyBusinessProvisioningLivelihoodPolitical scienceFood securityEconomicsPublic economicsEconomic growthAgricultureGeographyEngineering

Abstract

fetched live from OpenAlex

In this perspectives piece we ask: is it possible for a national food policy to form the foundation for sustainable and equitable food systems in Canada? First, we argue that under the current settler government, such a policy does not provide this foundation. Second, we consider what is possible to achieve within the scope of a national food policy, recognizing our responsibility as settlers to hold our government accountable so policies do not exacerbate food system inequities. To mitigate some of the harmful effects of current food-related policy, we make three suggestions: 1) restrict land access based on capital, number of properties owned, acreage, and interest in food provisioning; 2) support relevant and culturally appropriate markets by divesting from industrial scale food chains, and re-invest in marginalized food provisioners; and 3) direct funding to diverse non-consumptive food networks rather than export-oriented agro-food industries. To be clear, these suggestions will not decolonize a national food policy; rather, we argue they present short-term actions within the current settler state to address some of the ways the Canadian government inhibits Indigenous food systems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
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 head0.071
GPT teacher head0.351
Teacher spread0.280 · 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.

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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Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicIndigenous Studies and EcologyFrench-language works237,207