Entrepreneurial Storytelling: Transforming Shame into Guilt for Emotional Emancipation
Bibliographic record
Abstract
Entrepreneurial storytelling is often celebrated as a pathway for marginalized individuals to overcome personal and societal constraints, offering avenues for economic empowerment and social mobility. In the context of recently housed LGBTQIAA2S+ individuals, such storytelling transforms personal shame into actionable guilt, fostering emotional empowerment and a reclamation of agency. However, this emancipatory potential is paradoxically constrained by the necessity to conform to prevailing market norms and institutional standards, pressuring entrepreneurs to adhere to oppressive structures that undermine their authenticity. This study critically examines the dual role of entrepreneurial storytelling among recently housed LGBTQIAA2S+ individuals, exploring how it simultaneously fosters emotional empowerment and perpetuates systemic inequalities. Utilizing a sociological framework on emotional embeddedness and capital, the findings reveal that while entrepreneurial storytelling can empower, it often reinforces the systemic barriers it aims to dismantle by compelling conformity to oppressive norms. The research underscores the importance of developing more inclusive entrepreneurial models that validate diverse identities and challenge existing barriers, thereby enhancing the true emancipatory potential of entrepreneurship for marginalized communities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".