Navigating Inequality in Entrepreneurship: Strategies and Evaluations of Marginalized Entrepreneurs
Bibliographic record
Abstract
Accessing resources, and subsequent success, is not equal for all entrepreneurs. Questions about disparities in entrepreneurship have garnered major attention in the literature. Marginalized individuals face pervasive barriers as a result of stakeholders and investors’ biases. While past research has shown inherent barriers posed by marginalized identities, scant research has examined the strategic actions of both the entrepreneurs themselves and the supporting organizations in shaping these marginalized entrepreneurs’ journeys and outcomes. Thus, our symposium aims to advance our understanding of how marginalized entrepreneurs and their supporting organizations, such as training programs and investment funds, navigate the challenges posed by marginalized identities. Our presenters explore various strategies and their effectiveness from the side of entrepreneurs and involved agencies in supporting marginalized entrepreneurs overcome challenges associated with their marginalized identities. We center around two interrelated questions: What strategies are employed by entrepreneurs who possess marginalized identities and the organizations that assist them in overcoming various challenges associated with marginalized identities? Under what conditions do these strategies successfully support marginalized entrepreneurs, and when do they not? Together, the presentations provide implications for social inequality and pose questions for future research, such as how narrative disclosure helps overcome stigma, how entrepreneurship can override negative status beliefs, how interactions between entrepreneurs and supporting agencies shape entrepreneurial journeys, how evaluation processes may fail to create a more level playing field for marginalized entrepreneurs, and what communication strategies towards marginalized entrepreneurs can effectively increase participation in training programs. Collectively, these papers underscore the resilience and resourcefulness of entrepreneurs from under-represented backgrounds and the importance of creating supportive ecosystems that acknowledge and address the unique challenges they face. Stigma Disclosure in Entrepreneurial Narratives among Justice-Impacted Individuals Author: Yixi Chen; Columbia Business School Author: Kylie Jiwon Hwang; Northwestern Kellogg School of Management Entrepreneurial Activity as a Way to Override a Stigmatized Immigrant Status Author: Sandra Portocarrero; Columbia Business School Author: Dan Jun Wang; Columbia Business School Playing A Serious Game: North Korean Refugees’ Journey to Become an Entrepreneur in South Korea Author: Yunjung Pak; U. of Alberta Author: Suntae Kim; Johns Hopkins Carey Business School Author: Simon (Seongbin) Yoon; U. of California, Irvine Author: Hyo Young Lee; Boston U. Questrom School of Business Evaluating entrepreneurial potential Author: Amisha Miller; NYU Stern Author: Siobhan O'Mahony; Boston U. The Impact of Communication Frames on Necessity Entrepreneurs’ Participation in Training Programs Author: Florencio F. Portocarrero; London School of Economics and Political Science Author: Vanessa Burbano; Columbia Business School Author: Michael White; Columbia Business School
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".