<scp>Introduction to the Special Issue on Economics and Strategy of Entrepreneurship</scp>
Bibliographic record
Abstract
This special issue is the outgrowth of a 2007 conference, Entrepreneurship: Strategy and Structure, sponsored by the NBER Working Groups on Entrepreneurship and Innovation Policy and the Economy.The aim of the conference and special issue are to provide a forum for rigorous frontier research on the microeconomic and institutional foundations of entrepreneurship, and the strategic and market consequences of entrepreneurial activity.The papers included in the Special Issue include a subset of papers presented at the conference as well as papers submitted as the result of a solicitation for the Special Issue from the journal.As with all special issues in JEMS, all of the articles in this issue have gone through the standard editing and refereeing process that is applied to all submissions to JEMS.We would especially like to thank Dan Spulber, Josh Lerner, and Bob Strom for their help throughout the process, and the Kauffman Foundation for
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.165 | 0.083 |
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".