Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How should Theory-based entrepreneurs search for strategies to implement their ideas? The theory-based view of strategy posits that decision makers hold key conjectures about their path to success and use theory to understand and test beliefs underlying those conjectures. This causal framework also has implications for entrepreneurial search: the process by which entrepreneurs uncover strategies to implement their ideas. In this paper, we develop a Bayesian model where entrepreneurs update their beliefs as they conduct entrepreneurial search. We find several optimal behaviors for Theory-based entrepreneurs such as reverting to a previous strategy after finding a relatively poor strategy and continuing to search after finding a relatively good strategy, which are missing when entrepreneurs lack such a theory-based approach. As our theoretical predictions align with examples of successful entrepreneurs, our findings both provide a method to empirically identify Theory-based entrepreneurs and demonstrate the usefulness of applying the theory-based view to entrepreneurial behavior more generally.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it