Informing Canadian Innovation Policy Through a Decolonizing Lens on Indigenous Entrepreneurship and Innovation
Bibliographic record
Abstract
While Indigenous entrepreneurship is associated with significant economic promise, Indigenous innovation continues to be invisible in Canadian policy contexts. This article examines how Indigenous entrepreneurial activities are framed in government policy, potentially leading to another wave of active exploitation of Indigenous lands, peoples, and knowledges. The article first discusses the concepts of Indigenous entrepreneurship and innovation through a decolonizing lens, drawing links to education. Then, it provides a set of rationales for why governments need to re-think and prioritize Indigenous entrepreneurship. Next, it maps the current federal government initiatives in this policy sector. Drawing from the Indigenous entrepreneurship ecosystem approach (Dell & Houkamau, 2016; Dell et al., 2017), the article argues that a more comprehensive policy perspective guiding Indigenous entrepreneurship programs should inform Canadian innovation policy. Individual voices from 13 Indigenous entrepreneurs in Manitoba point to three core issues: (a) relationships with the land and the community; (b) the relevance of (higher) education and training; and (c) the importance of cultural survival and self-determination. The article makes an argument for a systemic decolonizing change in how Indigenous innovation is approached in government policyand programs, supported by the work of higher education institutions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".