Strategic Alliances in Firm-Centric and Collective Contexts: Implications for Indigenous Entrepreneurship
Bibliographic record
Abstract
How might diverse and often conflicting knowledge and belief structures and practices be mobilized into legitimate approaches for people looking to address the need for heightened responsible and sustainable entrepreneurial action by business organizations; humanizing the role of business in development? To answer this question, we explore two previously unconnected but aligned streams of literature: (i) work on strategic business alliances in general (R1); and (ii) work on corporate/Indigenous community partnerships specifically (R2). A systematic literature search identified 300 papers on the topics in total. We selected 39 general and 23 Indigenous-specific papers for review using a guiding classification matrix to determine principal themes and concepts. Both streams of literature were reviewed, and an approach was developed to identify areas where the empirical observation of Indigenous partnerships provides a contribution to the theory and practice of Indigenous entrepreneurship within the realm of strategic alliance formation, and vice versa. The paper concludes with a discussion of dissimilarities in the two streams of literature and maps out avenues for future research into strategic alliances involving corporate responsibility and sustainability (CRS), approaches based on Indigenous belief and value systems, and Indigenous entrepreneurship.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".