A predator–prey model of knowledge spillovers and entrepreneurship
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
Abstract Knowledge spillovers are known to generate positive benefits for entrepreneurs, but may come at the expense of knowledge creation by the incumbent firms which generate them. This article develops a predator–prey model of knowledge spillovers which captures the interdependence between idea‐creating incumbents and knowledge spillover‐appropriating entrepreneurs. The values of the model's parameters determine whether these two populations of firms settle down in a stable equilibrium; cycle over time with entrepreneurs doing well when incumbents do badly and vice‐versa; or drive each other to extinction. This sheds light on disparate industry life cycle patterns observed in previous research and generates some novel insights relating to public policy. In particular, the model suggests that governments ought to adopt a dynamic policy stance, initially implementing policies which favor incumbents before shifting their intervention efforts toward encouraging entrepreneurs. Copyright © 2010 Strategic Management Society.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it