Making Their Way in the Mainstream: Indigenous Entrepreneurs, Social Capital and Performance in Torontoâs Marketplace
Bibliographic record
Abstract
For ethnic entrepreneurs, it is vitally important to be able to move fluidly through boundaries between ethno-racial groups. Social activities on both sides of a boundary increase access to opportunities, needed resources and advantageous contacts in mainstream marketplaces. In Canada, men of European descent disproportionately hold positions of advantage and authority in mainstream marketplaces. Entrepreneurs wishing to do business in these markets must foster ties with well placed European Canadians, yet research shows that ethnic minorities are typically shut out of these important and advantageous networks. Through three publishable papers, this dissertation considers the unique case and place of Indigenous entrepreneurs in Toronto, Canada. They are a population discriminated against for centuries, while at the same time a fundamental part of the creation of Canadian society. This dissertation asks whether and how Indigenous entrepreneurs can move effectively across ethnic boundaries and participate in multiple groups and settings. \nMore specifically, these three papers explore the factors and macro social structures that contribute to the development of diverse networks and cultural capital within Indigenous and Euro-Canadian worlds, and the effects of social and cultural capitals on performance in Toronto's mainstream marketplace. While current theory explores the ability of some individuals to move between groups and across boundaries, research does not exist to test these assertions. This dissertation provides then, an initial case study of boundary spanning behaviour and the first effort at exploring Indigenous entrepreneurs in that role. Findings do indeed show that respondents instrumentally develop and maintain diverse cultural and social capital. Further, some forms of social capital contribute substantially to successful performance in Toronto's mainstream marketplace.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".