MétaCan
Menu
Back to cohort
Record W4398770304 · doi:10.1080/22423982.2024.2359161

Traditional food security and food sovereignty in the coastal region of South-Central Alaska

2024· article· en· W4398770304 on OpenAlexaff
Joseph Nyholm, Amanda Walch, Leslie Redmond

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood securityGeographyFood sovereigntySovereigntyFisheryFood supplyEnvironmental protectionOceanographyPolitical scienceAgricultural economicsBiologyPoliticsArchaeologyGeologyAgricultureEconomics

Abstract

fetched live from OpenAlex

A food assessment questionnaire was completed by Alutiiq and Eyak peoples of the Chugach Region of Alaska in 2016-2017. This questionnaire, conducted by the Chugach Regional Resource Commission, gathered 87 responses from adults residing in seven communities. The questions related to traditional food systems, food security, and food sovereignty and were organised into six sections: Community Food Resources, Diet and Health, Culture, Organisation and Governance, Food Resources, and Natural Resources and Environment. Nine questions directly addressed food sovereignty. Results revealed the importance of traditional food sources in the communities, foods that are not readily available or are difficult to access, resources that are useful to improve traditional food security, health problems that are perceived to be caused or exacerbated by the lack of traditional foods in the area, traditional foods commonly consumed, and barriers from accessing traditional foods. Additionally, recommendations for improving food systems and addressing barriers are provided.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.351
Teacher spread0.293 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Circumpolar HealthSame topicIndigenous Studies and EcologyFrench-language works237,207