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Record W4392815663 · doi:10.29173/jaed298

Community Economic Development With Neechi Foods: Impact on Aboriginal Fishers in Northern Manitoba, Canada

2011· article· en· W4392815663 on OpenAlexaffabout
Durdana Islam, Shirley Thompson

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsIndigenousBusinessFish <Actinopterygii>GeographyEconomic growthMarketingFisheryEconomics

Abstract

fetched live from OpenAlex

Neechi Foods Co-op located in the north end of Winnipeg, Canada is an ideal example of an Aboriginal community economic development initiative. This grocery store has been operating for over 21 years and is an associate member of the Federated Co-operatives Ltd. Neechi Foods is committed to providing quality products and services to ensure a high degree of customer satisfaction and retention, building a strong cooperative, and promoting community economic development and opportunities for Aboriginal peoples. Neechi Foods Co-op sells freshly prepared bannock, wild rice, wild blueberries, freshwater fish, and other Indigenous specialty foods, home-made deli products, conventional grocery items and Aboriginal crafts, books and music. The co-op has been commercially self-reliant and profitable despite severe economic crises in its surrounding neighbourhood. In 2009-2010 financial year, annual sales of Neechi Foods Co-op reached over $600,000. Neechi Foods Co-op is expanding its business and building the Neechi Commons Co-operative business complex which will start operation in 2012.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.002
Scholarly communication0.0030.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.281
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 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

Citations2
Published2011
Admission routes2
Has abstractyes

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