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Record W1851675874

Toward Sustainable Development in the North: Exploring Models of Success in Community-Based Entrepreneurship

2014· article· en· W1851675874 on OpenAlexaffabout
David Zhang, Lee Swanson

Bibliographic record

VenueNorthern review · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPanacea (medicine)Extant taxonBenchmarkingEntrepreneurshipRegional scienceSustainable developmentSet (abstract data type)Community developmentPolitical scienceEconomic growthSociologyGeographyEnvironmental planningBusinessEconomicsMarketingComputer science
DOInot available

Abstract

fetched live from OpenAlex

This article intends to achieve a better understanding of economic and social development in the remote, northern regions in Saskatchewan. The authors reviewed development models that were employed by various communities. By benchmarking the practices documented and reported in the extant literature, the authors discussed the need for comparative empirical research for further development of the theory on northern development. Following a comparative case analysis method, the authors selected cases that were theoretically and empirically comparable to Northern Saskatchewan, re-examined the reported relationships among relevant factors, and categorized the themes regarding developmental strategies and their resulting outcomes. These cases were from communities in Canada, the US, and Northern Scandinavia. While there was general agreement on what constituted “good practices” for regional economic and social development, there was no consensus or panacea for success. Each case was heavily embedded in a set of contextual circumstances. Some communities had undertaken different strategies to achieve the same kind of outcomes, while similar strategies have produced drastically different results in other communities.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.325
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.373
Teacher spread0.162 · 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.

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
Published2014
Admission routes2
Has abstractyes

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