Identity Strain, Gender Differences, and Coping Mechanisms Within Entrepreneurial Careers
Bibliographic record
Abstract
This symposium examines various contexts to further develop the burgeoning interest in entrepreneur identity development. The theme investigates processes surrounding individual identity development, specifically when the individual identifies as an entrepreneur or chooses an entrepreneurial career. Each paper within the symposium discusses career choices among entrepreneurs with identity conflicts. These topics hold relevance for the Entrepreneurship, Organizational Behavior, and Careers subdivisions of the Academy of Management. Entrepreneur dual identity integration and implications for creativity and venture success Presenter: Siran Zhan; U. of New South Wales Presenter: Marilyn Ang Uy; Nanyang Technological U., Singapore Presenter: Ying-yi Hong; Nanyang Technological U. Chef or business owner? Negotiating identity conflicts in the culinary industry Presenter: Daphne Ann Demetry; McGill U. Presenter: Rachel Doern; U. of London, Goldsmiths College Nurturing an entrepreneurial identity: Entrepreneurial employees and innovative workplace climates Presenter: J. Jeffrey Gish; U. of Oregon Presenter: David Ross Marshall; U. of Dayton Presenter: Scott Seibert; U. of Oregon The role of identity in work-life balance for entrepreneurs with hobby-inspired businesses Presenter: Sabrina DeeAnn Volpone; U. of Colorado Boulder Presenter: Sara Jansen Perry; Baylor U. Presenter: Cristina Rubino; California State U., Northridge Presenter: B. Lindsay Brown; U. of Georgia
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".