Policy-making for Indigenous entrepreneurship: towards an inclusive approach
Bibliographic record
Abstract
This paper investigates the success level of Indigenous entrepreneurship (IE) policies, examines if there is an international dimension to the successes and failures of IE policies, and synthesises lessons from both successful and unsuccessful policies to lead towards a more inclusive approach for the future. Using a narrative systematic review, our findings show that while some IE policies have succeeded, others have either been ineffective, ill-conceived or, in a few cases, have led to worse/negative outcomes for Indigenous entrepreneurs. We unpack lessons from the successful and failed policies and provide suggestions for future policy and research.
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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 | 0.115 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| 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".