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Record W4239221121 · doi:10.1504/ijesb.2018.096177

Social entrepreneurship by cooperative: examining value chain options of an indigenous fisherman's co-op

2018· article· en· W4239221121 on OpenAlexaffabout
A. K. M. Shahidullah, Durdana Islam

Bibliographic record

VenueInternational Journal of Entrepreneurship and Small Business · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousEntrepreneurshipValue (mathematics)Value chainGovernment (linguistics)Social entrepreneurshipMarketingBusinessIntervention (counseling)Focus groupCommunity developmentWork (physics)Public relationsEconomic growthSociologyEconomicsSupply chainFinancePolitical science

Abstract

fetched live from OpenAlex

Indigenous cooperative supporting community development objectives has potential to flourish as a social enterprise. Our study draws on social value creation and value chain to find value addition opportunities of a community-based cooperative. Field study was conducted in Norway House Cree Nation community, Northern Manitoba, Canada, adopting qualitative case study methods. We surveyed fisher-households and cooperative members. As well, we conducted semi-structured interviews with experts, government officials, NGO personnel, fish distributors, retailers, and marketing agents. We also held two focus group discussions in the community. We observed that the studied cooperative operates only as a supplier to the primary market. Results show that intervention in the upstream value chain with establishment of modern processing facilities would ensure cooperatives' participation in the secondary markets, create employment opportunities in the community, and enhance its capacity for further social contributions. We conclude that value chain intervention, if made at the community level where producers work corporately as a primary suppliers under a cooperative, creates further value in the society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.291
Teacher spread0.258 · 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 teacher head, not a consensus.

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

Citations4
Published2018
Admission routes2
Has abstractyes

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