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Record W4205528607 · doi:10.1080/22423982.2021.2024679

Impact of Assistance Programs on Indigenous Ways of Life in 12 Rural Remote Western Alaska Native Communities: Elder Perspectives Shared in Formative Work for the “Got Neqpiaq?” Project

2022· article· en· W4205528607 on OpenAlexaboutno aff
Kathryn A. Ohle, Kathryn R. Koller, Lucinda Alexie, Flora Lee, Lea Palmer, Jennifer Nu, Timothy K. Thomas, Andrea Bersamin

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institute of Nursing Research
KeywordsIndigenousGovernment (linguistics)Work (physics)Culturally appropriateFood securityFormative assessmentTraditional knowledgeEconomic growthPolitical sciencePublic relationsSocioeconomicsGerontologyGeographySociologyMedicinePedagogyAgricultureEngineering

Abstract

fetched live from OpenAlex

For more than 50 years, government programmes in the USA have been in place to help those in need have consistent access to food and education. However, questions have surfaced regarding whether or not these support impact traditional ways, such as cultural activities, food preferences, and overall health, particularly for Indigenous populations. In this paper, we share insights voiced by Alaska Native Elders in the Yukon-Kuskokwim region of Alaska and their perceptions of regulations, assistance, and the impact government assistance programmes have had on their culture. Elders raised concerns so that those administering these programmes will consider how best to meet food security and education needs without interfering with Indigenous cultural practices and traditional lifestyle.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0030.002
Open science0.0010.007
Research integrity0.0010.002
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 head0.096
GPT teacher head0.423
Teacher spread0.327 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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