Managing Marginalized Identities for Entrepreneurial Success
Bibliographic record
Abstract
Prior work has shown that the observable social identities of entrepreneurs – such as their marginalized gender, race, religion, or social class – play a significant role in their ability to mobilize resources and successfully engage stakeholders (Fairlie & Robb, 2007; Freeland & Keister, 2016; Snellman & Younkin, 2021; Younkin & Kuppuswamy, 2018). Motivated by these findings, we propose a symposium to better understand the barriers faced by those from historically marginalized groups and examine how members of these groups manage their identities to overcome these barriers. Specifically, we considered three related questions. First, why and when do marginalized entrepreneurial identities lead to lowered resource mobilization and stakeholder evaluation? Second, what strategies do entrepreneurs with these identities and the institutions supporting them undertake to overcome obstacles that entrepreneurs may face due to these marginalized identities? Third, under what conditions are these identity-based entrepreneurial strategies effective, and under what conditions are they not? We hope our symposium will contribute to ongoing discussions about the presence of inequality among entrepreneurs. Our scholars will address these questions in a wide range of empirical contexts, including North Korean refugee entrepreneurs in South Korea, Muslim American entrepreneurs, and entrepreneurs in Morocco, using a diverse collection of qualitative, archival, and experimental methods. After providing comments and integrating lessons learned from the presenters, our discussant Martin Ruef, an authority on entrepreneurship among historically marginalized groups (see, for example, Ruef, 2010, 2014; Ruef & Grigoryeva, 2020), will lead a group discussion composed of prepared questions and audience question and answer session to synthesize understanding and outlining avenues for future research. Organizational Blueprints: What Does it Take to Build an Organization? Author: Tiantian Yang; U. of Pennsylvania Playing A Serious Game: North Korean Refugees’ Journeys to Becoming Entrepreneurs in South Korea Author: Suntae Kim; Johns Hopkins Carey Business School Author: Hyo Young Lee; - Author: Yunjung Pak; U. of Alberta Author: Simon (Seongbin) Yoon; U. of California, Irvine Managing Marginalized Entrepreneurial Identity to Create New Markets: A case of Muslim Entrepreneurs Author: Yun Ha Cho; U. of Michigan Author: Diana Jue-Rajasingh; Rice U. Author: William Reuben Hurst; U. of Michigan, Ross School of Business Accelerators or Brakes?: A Field Experiment on Encouraging Entrepreneurship in Morocco Author: Ouafaa Hmaddi; City College - City U. of New York Author: Peter Younkin; U. of Oregon
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".