Toward a Theory of Entrepreneurial Cognition: Rethinking the People Side of Entrepreneurship Research
Bibliographic record
Abstract
The failure of past “entrepreneurial personality”—based research to clearly distinguish the unique contributions to the entrepreneurial process of entrepreneurs as people, has created a vacuum within the entrepreneurship literature that has been waiting to be filled. Recently, the application of ideas and concepts from cognitive science has gained currency within entrepreneurship research, as evidenced by the growing accumulation of successful studies framed in entrepreneurial cognition terms. In this article we reexamine “the people side of entrepreneurship” by summarizing the state of play within the entrepreneurial cognition research stream, and by integrating the five articles accepted for publication in this special issue into this ongoing narrative. We believe that the constructs, variables, and proposed relationships under development within the cognitive perspective offer research concepts and techniques that are well suited to the analysis of problems that require better explanations of the contributions to entrepreneurship that are distinctly human.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.058 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".