Bibliographic record
Abstract
Entrepreneurial legitimacy scholars have established legitimacy as “the most pressing issue” facing early-stage entrepreneurs (Lounsbury & Glynn, 2001: 550) and studied strategies by which founders can develop impressions of themselves and their businesses as being desirable and appropriate in front of audiences ranging from peers and investors to government agencies, to corporate gatekeepers, to early adopters. In this essay, we introduce the entrepreneur themself as an important audience making legitimacy judgments and specify how extant theorizing suggests evaluations of self-legitimacy are linked to entrepreneurial behavior. Adopting a social cognitive approach (Bandura, 1986), we define and develop a dynamic framework for entrepreneurial self-legitimacy (ESL) wherein, over time, individuals experience various states along a spectrum that integrates the independently developed concepts of impostor phenomenon at one extreme and hubris at the other. We illustrate the mindsets and behavioral tendencies that result from low and high ESL, emphasizing that both ends of the spectrum have ‘bright’ and ‘dark’ sides. In consolidating rich constructs into a common framework, our work contributes an important new perspective to conversations on entrepreneurial legitimacy and entrepreneurial social cognition. We finish by outlining avenues for future research on ESL, including the capacity of such work to practically support the trajectories of entrepreneurs.
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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 | 0.011 |
| 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 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".