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Record W4282936715 · doi:10.1177/10422587221096907

Indigenous Entrepreneurship and Venture Creation: A Typology of Indigenous Crowdfunding Campaigns

2022· article· en· W4282936715 on OpenAlexaff
Annaleena Parhankangas, Rick Colbourne

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsCarleton University
Fundersnot available
KeywordsIndigenousEntrepreneurshipTypologyCitizen journalismVenture capitalValue (mathematics)Social capitalSociologyCapital (architecture)Economic growthPolitical sciencePublic relationsMarketingBusinessEconomicsSocial scienceGeographyFinance

Abstract

fetched live from OpenAlex

Indigenous entrepreneurship is a process of drawing value from community-based resources (people, land, capabilities, culture, etc.) and contributing value back that is responsive to a community’s particular set of socioeconomic conditions (Colbourne, 2017a; Jack & Anderson, 2002; Kenney & Goe, 2004: 699). The advent of crowdfunding pointed to the potential of digital platforms to facilitate socioeconomic change through ameliorating disparities in access to entrepreneurial financing for marginalized communities. Thus, crowdfunding represents an opportunity for Indigenous peoples to access capital; showcase their ventures; and assert their right to design, develop, and maintain Indigenous-centric institutions. To investigate the emancipatory potential of Indigenous crowdfunding campaigns, we conducted a non-participatory netnographic explorative study that analyses over 1300 Indigenous campaigns launched between 2010 and 2020. Based on our findings, we develop a typology of Indigenous emancipatory crowdfunding across four orientations: (i) commercial, (ii) cultural, (iii) community, and (iv) activist campaigns.

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0050.010
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.258
Teacher spread0.239 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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