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Record W2054969472 · doi:10.1155/2012/130945

“We Are Not Being Heard”: Aboriginal Perspectives on Traditional Foods Access and Food Security

2012· article· en· W2054969472 on OpenAlexafffundabout
Bethany Elliott, Deepthi Jayatilaka, Contessa Brown, Leslie Varley, Kitty Corbett

Bibliographic record

VenueJournal of Environmental and Public Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser UniversityProvincial Health Services Authority
FundersUniversity of TorontoProvincial Health Services Authority
KeywordsMainstreamFood securityFood insecurityPolitical scienceOrder (exchange)BusinessPublic relationsEconomic growthSociologyGeographyAgricultureEconomics

Abstract

fetched live from OpenAlex

Aboriginal peoples are among the most food insecure groups in Canada, yet their perspectives and knowledge are often sidelined in mainstream food security debates. In order to create food security for all, Aboriginal perspectives must be included in food security research and discourse. This project demonstrates a process in which Aboriginal and non-Aboriginal partners engaged in a culturally appropriate and respectful collaboration, assessing the challenges and barriers to traditional foods access in the urban environment of Vancouver, BC, Canada. The findings highlight local, national, and international actions required to increase access to traditional foods as a means of achieving food security for all people. The paper underscores the interconnectedness of local and global food security issues and highlights challenges as well as solutions with potential to improve food security of both Aboriginal and non-Aboriginal peoples alike.

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)0.009
metaresearch head score (Gemma)0.008
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 score0.713
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0370.037
Scholarly communication0.0090.005
Open science0.0020.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.378
Teacher spread0.298 · 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

Citations79
Published2012
Admission routes3
Has abstractyes

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