Building Entrepreneurial Architectures: A Conceptual Interpretation of the Third Mission
Bibliographic record
Abstract
Universities are increasingly being challenged to become more socially and economically relevant institutions under the guise of the so-called ‘Third Mission’. This phenomenon, articulated in policy, has prompted the emergence of a growing literature documenting the evolution of the contemporary university, and specifically addressing the Third Mission and university entrepreneurship; however, it remains at once both too broadly conceptualised and overly fragmented. Thus, as the scope of university entrepreneurship widens to include ever more forms of engagement, the Third Mission remains under-theorised. Drawing together these streams of literature on the contemporary university, the concept of ‘entrepreneurial architecture’ is employed to develop a more nuanced perspective. Based on a study of UK higher education institutions, this article builds on Burns's (2005) notion of ‘entrepreneurial architecture’ to understand the internal dynamics that underpin the coordination and consolidation of the Third Mission. The Third Mission has been politically created through numerous (prescriptive) funding programmes; however, the next phase of the Third Mission demands an understanding beyond prescription. The concept of entrepreneurial architecture provides a grounded theoretical contribution to the study of university entrepreneurship, while also offering institutions and policy makers a pragmatic approach to institutional development in the context of the Third Mission.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".