Entrepreneurial Action by Métis and First Nations entrepreneurs in Saskatchewan: Similarities and differences with established notions of Entrepreneurial Action
Bibliographic record
Abstract
Entrepreneurial actions, i.e., activities like hiring, marketing, financing, hustling (even bribing!), etc., requisite for building small businesses are less studied than antecedent opportunity recognition processes. The two most common form of contexts in which entrepreneurial actions are studied are opportunity-driven (Silicon-Valley type) and necessity-driven (poverty contexts). While there is a fair amount of research on community-based and band-driven Indigenous entrepreneurship, less is known about entrepreneurial actions by individual self-employed Métis and First Nations entrepreneurs in Canada/ Turtle Island. Métis and First Nations entrepreneurs face a differential set of obstacles in their pursuit for economic self-determination compared to their non-Indigenous counterparts. This dissertation endeavours to understand entrepreneurial actions undertaken by individual Métis and First Nations entrepreneurs and their similarities and differences with dominant notions, more specifically the extant notions of opportunity-driven and necessity-driven entrepreneurial actions. I do so abductively by leveraging qualitative methods, in the context of Métis and First Nations self-employed entrepreneurs in the Canadian Prairies (more specifically, Saskatchewan). Findings highlight that the entrepreneurial actions of Métis and First Nations entrepreneurs differ compared to dominant notions along three dimensions, namely – motivation, liabilities, and the actions themselves. I submit that this has both theoretical and practical implications as my findings make a case for explicitly accounting for a role of self-regulatory coping and volition as foundational micro-components of entrepreneurial action, in addition to knowledge and motivation already prescribed in extant literature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".