Indigenous entrepreneurial capital: a grounded theory study
Bibliographic record
Abstract
Rationale: Economic development among Canada’s Indigenous peoples is considered an essential part of reconciliation, evidenced by the Truth and Reconciliation Commission of Canada’s (TRC) Call to Action 92. A key driver of economic development is entrepreneurial venturing, and yet an understanding of Indigenous entrepreneurship has yet to be clarified. Knowledge of how Indigenous practitioners’ view their participation in these activities is beneficial for scholars to understand the sector, and beneficial to practitioners being part of the conversation. Parameters, regulatory, social, or otherwise, should be informed by Indigenous views if Call to Action 92 is to be respected. Aim To explore Indigenous participants’ viewpoints on their participation in entrepreneurship and how they define their activities in comparison to academic definitions. Outputs and conclusions Ten case studies were developed, covering a range of entrepreneurs and their activities. Topics for these case studies were derived from priorities described by participants which enhanced and informed the resulting model. The model emerged from application of grounded theory processes within the context of Two-Eyed Seeing, an Indigenous research approach that balanced western and Indigenous perspectives. The model, generated from the practitioners’ viewpoints on defining Indigenous entrepreneurship, revolved around the core category of engaging with entrepreneurial capital, which itself was derived from four subcategories: social capital, cultural capital, human capital, and financial/economic capital. This model interpreted participant efforts to define Indigenous entrepreneurship (IE) and led to the tentative definition drawn from participant worldviews of IE as Indigenous entrepreneurship is defined as an holistic process intertwining identity, experiences, and multiple bottom lines, reflected in how practitioners engage with entrepreneurial capital that sees the journey as being as important as the goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".