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Indigenous controlled joint ventures and the transformation of opportunity structure constraints

2017· article· en· W2766898347 on OpenAlexaffabout
Moses Gordon, Bob Kayseas, Peter W. Moroz

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsFirst Nations University of Canada
Fundersnot available
KeywordsEmbeddednessIndigenousContext (archaeology)Agency (philosophy)EntrepreneurshipResource (disambiguation)Structure and agencySociologyNatural resourceProcess (computing)BusinessEconomic systemKnowledge managementPolitical scienceEconomicsEcologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper examines the role that strategic alliances play in the formation of new ventures within a specific context, namely that of a First Nations community engaged in the Canadian natural resources sector. Grounded theory and a single case study approach was utilized to provide a deep understanding of the processes involved in the creation of a distinct new Aboriginal organizational form. A guiding framework is used which includes the concept of mixed embeddedness, resource based theory and agency theory. Insight into the process and alloyed motivations of Indigenous entrepreneurship is gained that extends our understanding of opportunity structures wrought by the legacies of colonialism. A theory of context is developed building upon the aspects of collective agency, resources and a view that extends the concept of social embeddedness to encompass strategic alliances as entrepreneurial tools for social transformation.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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