Decolonizing Entrepreneurship: Time to Open Both Eyes
Bibliographic record
Abstract
We address our role as educators and researchers of entrepreneurship in ensuring that everything we do today is aimed at reconciling the relationship between Indigenous and non-Indigenous people and restoring balanced relationships. Based on recognition of the importance of Indigenous knowledge for reconciliation and the value of Indigenous knowledge in a more holistic and comprehensive understanding of entrepreneurship to make better scientific and educational decisions, we describe a brief introduction and partial explanation of our lack of attention and offer justification for checking our assumptions about entrepreneurship and decolonizing our research and teaching. We offer a brief introduction to examples of Indigenous conceptual frameworks of ethical and appropriate informed pluralism that allow ways of knowing, being, and doing. Finally, we offer some suggestions for scholars in our field in pursuit of decolonizing their minds and work.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".