A Framework for Exploring Heterogeneity in University Business Incubators
Bibliographic record
Abstract
Abstract Globally, business incubators and accelerators have been embraced as important mechanisms to support the growth and development of new ventures. Several typologies have been proposed as a means of classifying their alternate forms. Within these typologies university business incubators (UBIs) are often recognized as a separate, but homogeneous class. Yet taking an isomorphic approach fails to acknowledge that differences among UBIs have implications for how they function and how their performance should be evaluated. Performance evaluation is an important issue as universities come under increasing pressure to demonstrate that the public funding they receive in support of their incubation activities is being put to good use. This paper offers a new perspective for the study of UBIs that focuses on their heterogeneity. We develop a framework that posits two competing narratives for UBIs, commercial and educational, that represent extremes on a continuum where hybrid configurations are also possible. Our framework demonstrates that these narratives offer a systematic explanation of differences in UBIs, have implications for performance evaluation, and suggest directions for future research aimed at advancing our understanding of variation in the way UBIs are configured and managed.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".