Decolonizing Scaffolding: Learning from First Nations’ Resurgence to Recalibrate Entrepreneurship
Bibliographic record
Abstract
Abstract Indigenous communities are resurging and harnessing this momentum to reshape their social world. As they reclaim their cultural resources, rights, and identities, they gain control over how and whether to engage with Western social structures. Using the metaphor of metal frame and grass basket materials, we conceptualize how First Nations communities mobilize scaffolding materials from different social realities and innovatively combine them to shape and reshape their social world. We theorize that the degree to which a community integrates different scaffolding materials is consequential for whether and how the community will engage with outsiders. We learn from First Nations communities across northern Turtle Island (Canada) by running a cluster analysis and identifying three different ways communities construct scaffolding using Indigenous and Western materials. Then, using a unique dataset of 240 Canadian First Nations communities, we use those clusters to predict the likelihood a given community will engage in a partnership agreement with an outsider, in this case a non‐Indigenous mining company, and when they might do so. This analysis highlights a kind of entrepreneurial activity, the construction and use of scaffolding, and a context that is overlooked by mainstream entrepreneurship scholars and management scholars in general. We aim to contribute to the recalibration of entrepreneurship literature through a decolonial lens.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".