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Record W4200432356 · doi:10.3389/fcomm.2021.749944

Safe Food, Dangerous Lands? Traditional Foods and Indigenous Peoples in Canada

2021· article· en· W4200432356 on OpenAlexafffundabout
Tabitha Robin, Kristin Burnett, Barbara Parker, Kelly Skinner

Bibliographic record

VenueFrontiers in Communication · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of WaterlooLakehead UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousState (computer science)SovereigntyFood sovereigntyIntervention (counseling)Political scienceColonialismGeographyFood securityEnvironmental protectionBusinessEnvironmental ethicsLawEcologyMedicineArchaeology

Abstract

fetched live from OpenAlex

There is a deep and troubling history on Turtle Island of settler authorities asserting control over traditional foods, market-based and other introduced foods for Indigenous peoples. Efforts to control Indigenous diets and bodies have resulted in direct impacts to the physical, emotional, mental and spiritual well-being of Indigenous peoples. Food insecurity is not only a symptom of settler colonialism, but part of its very architecture. The bricks and mortar of this architecture are seen through the rules and regulations that exist around the sharing and selling of traditional or land-based foods. Risk discourses concerning traditional foods work to the advantage of the settler state, overlooking the essential connections between land and food for Indigenous peoples. This article explores the ways in which the Canadian settler state undermined and continues to undermine Indigenous food sovereignty through the imposition of food safety rules and regulations across federal, provincial, and territorial jurisdictions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0220.007
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.040
GPT teacher head0.295
Teacher spread0.256 · 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 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

Citations30
Published2021
Admission routes3
Has abstractyes

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