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Record W2286020094

Creating Wealth and Employment in Aboriginal Communities

2005· article· en· W2286020094 on OpenAlexaboutno aff
Stelios Loizides, Wanda Wuttunee

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)AccountabilityPoliticsCommunity economic developmentBusinessCorporate governanceCommunity developmentUnemploymentJob creationPublic relationsEconomic growthPlan (archaeology)Political scienceEconomicsFinanceLabour economicsGeography
DOInot available

Abstract

fetched live from OpenAlex

In light of the high unemployment rate in Canadian Aboriginal communities, it is important to examinethe role that wealthand job creation play in the economic development of these communities.Inorder to become self-reliant, these communities are establishing community-owned firms that seek to create wealth and employment but still maintain the traditional values of the communities. Data used in this report were collected from a literature review in additionto interviews with Aboriginal leaders in ten geographically distinct communities.Through this case study approach, six key factors that contribute to the success of Aboriginal community-owned firms are identified.These factors include the following: (1) strong leadership andvision; (2) a strategic community economic development plan; (3) access tocapital, markets, and management expertise; (4) good governance and management;(5) transparency and accountability; and (6) the positive interplay of businessand politics. The ten firms analyzed in this report have been successful. The profitsgenerated by the businesses are reinvested in the business and thecommunity. This community capitalism type of business development has provided clear benefits for these communities and should be considered by other Aboriginal communities seeking to become self-reliant.(SRD)

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.377
Teacher spread0.355 · 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 designTheoretical or conceptual
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

Citations6
Published2005
Admission routes1
Has abstractyes

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