A Framework for Exploring Heterogeneity in University Business Incubators
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 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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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