I Am and I Am Not: Ambivalence in Entrepreneur Identification
Bibliographic record
Abstract
Research on entrepreneur identity has focused on understanding how and when individuals see themselves as an entrepreneur and scholars have argued that individuals either clearly identify with the term ‘entrepreneur’ or they disidentify with it. However, the literature hints at instances of ambivalent identification, i.e., when individuals may identify with certain facets of the term and disidentify with others. Using qualitative data from 29 founders of businesses across the United States, Canada, and Australia, we delve deeper into this phenomenon and find that individuals are ambivalent not only with reference to their identity as an entrepreneur but also as to how they want others to perceive them, i.e., their image. We develop a two-stage model of entrepreneur ambivalence that captures these findings and illustrates the reasons why individuals may identify, disidentify, or ambivalently identify with the term ‘entrepreneur’ and what leads them to project an image that is either in sync with or misaligned with their identity as an entrepreneur. Our findings have important theoretical and practical implications for entrepreneurs and agencies focused on supporting and encouraging them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".