Bibliographic record
Abstract
Research Summary This paper develops an integrated framework linking the nature of the entrepreneurial choice process to the foundations of entrepreneurial strategy. Because entrepreneurs face many alternatives that cannot be pursued at once, entrepreneurs must adopt (implicitly or explicitly) a process for choosing among entrepreneurial strategies. The interplay between uncertainty and learning has the consequence that commitment‐free analysis yields multiple, equally viable alternatives from which one must be chosen. This endogenous gap between optimization and choice is a central paradox confronting entrepreneurs. Resolving this allows for a reformulation of the foundations of entrepreneurial strategy, emphasizing the role of choice rather than the centrality of the strategic environment. Managerial Summary The central strategic challenge for an entrepreneur is how to choose: entrepreneurs often face multiple potential strategies for commercializing their idea but due to the constraint of limited resources, cannot pursue them all at once. At the same time, entrepreneurs are venturing into new domains and as such, must choose under conditions of high uncertainty with only noisy learning available. This paper explores the interplay between these unique conditions that shape the entrepreneurial choice process, finding that often, the process will not yield a single best strategy but instead several equally attractive strategic alternatives. A key implication is that entrepreneurs cannot simply choose what not to do, but instead must proactively decide which equally viable alternatives to leave behind when choosing an entrepreneurial strategy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".