MétaCan
Menu
Back to cohort
Record W2748761327 · doi:10.13034/jsst.v10i1.186

The Adverse Effects Government Initiatives Have on Aboriginal Food Insecurity

2017· article· en· W2748761327 on OpenAlexvenueaboutno aff
Chinonso Ekeanyanwu

Bibliographic record

VenueJournal of Student Science and Technology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFood securityFood sovereigntyGovernment (linguistics)Political sciencePopulationEconomic growthPovertyScarcityFood insecuritySociologyGeographyAgricultureEconomicsLawEcology

Abstract

fetched live from OpenAlex

This research paper focuses on the attempts of the Canadian government to deal with food scarcity in the Indigenous community. Despite the many efforts of the government to make amends with the Indigenous population, they have some of the highest rates of poverty demographically in Canada. Food scarcity is a major topic when talking about Indigenous people because many live in areas where there is no access to healthy affordable food. Many do not have access to traditional food and are unable to exercise their right as Indigenous people to fish and hunt. Within this paper, three pertinent examples are explored: first, the lack of regard for Indigenous food sovereignty; second, the issue of fishing legislations; finally, food security initiatives in the North. Far from meaningfully, addressing food insecurity, nutritional programs designed by the federal government have often exacerbated the issue. This is likely due to the lack of involvement from the Indigenous community and their leaders in decision-making. By incorporating the Indigenous community, food security laws and programs made for Indigenous people have the potential to actually have a positive impact on the Indigenous community. Ce document de recherche se concentre sur les efforts du gouvernement canadien d’affronter la pénurie alimentaire dans la communauté autochtone. Malgré les nombreux efforts déployés par le gouvernement pour aider la population autochtone, leur niveau de pauvreté est parmi les plus élevés au Canada. La pénurie alimentaire est un problème majeur en ce qui concerne les Autochtones, car beaucoup d’entre eux vivent dans des zones qui n’ont pas accès à des aliments sains et abordables. Beaucoup n’ont pas accès à la nourriture traditionnelle et sont incapables d’exercer leur droit en tant que peuple indigène de pêcher et de chasser. Dans ce document, trois exemples pertinents sont explorés: premièrement, le manque de respect pour la souveraineté alimentaire indigène; deuxièmement, le problème des législations de pêche; et en fin, les initiatives de sécurité alimentaire au Nord. Pour tenter de remédier à l’insécurité alimentaire, les programmes nutritionnels conçus par le gouvernement fédéral ont souvent exacerbé la question. Cela est probablement dû au manque d’implication de la communauté autochtone et de ses dirigeants dans la prise de décision concernant ces programmes. En incorporant la communauté autochtone dans la discussion entourant les lois et les programmes de sécurité alimentaire, ils ont le potentiel d’avoir un impact réel et positif sur la communauté indigene.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.005
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.416
Teacher spread0.394 · 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

Labeled directly by 2 models reading the full record.

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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Student Science and TechnologySame topicIndigenous Studies and EcologyFrench-language works237,207