Social capital and co‐leadership in ethnic enterprises in Canada
Bibliographic record
Abstract
Purpose To identify the influence of ethnicity and ethnic social capital on entrepreneurial practices such as the co‐direction of a firm, and more particularly on aspects of venture creation, management, and business development. Design/methodology/approach The research was based on a field survey carried out in the cities of Montreal, Toronto, and Vancouver. The authors study the entrepreneurs who had partners in their firms. The sampling technique, known as “snowball sampling,” did not concentrate specifically on firms with co‐leadership structures, but targeted all entrepreneurs in the ethnic groups concerned; the interviewers asked respondents to identify other potential candidates in the same ethnic group. The participation rate was not measured systematically. Findings Co‐leadership, while fairly common and having a clear impact among Italian entrepreneurs, is not necessarily as popular in the other groups. Cultural features may have an influence here, and the structuring effects of the entrepreneur's social capital are certainly a factor. The findings helped build an emergent conceptual model to show the place of co‐leadership in the creation and development of social capital used in the management of ethnic firms. Research limitations/implications Public policy makers must take into account that trust and reciprocity will have an impact on the style of partnership selected. Other qualitative and quantitative data are needed to help understand the various factors and impacts of co‐leadership. Also, examination of the individual and joint inputs and outputs of the partners is an important and timely area of study. Practical implications This may have implications in designing public programs to help ethnic entrepreneurs. Originality/value This is one of the first studies to examine co‐leadership in the context of ethnic entrepreneurship. With the importance of immigration, this is crucial to understand how to help the success of ethnic businesses and therefore the integration of immigrants in our societies.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".