Municipal Entrepreneurship, Sources of Opportunity and Procurement for Innovation
Bibliographic record
Abstract
Numerous subfields of entrepreneurship, such as corporate entrepreneurship, international entrepreneurship and sustainable entrepreneurship have recently emerged as fields of scholarly interest. In this article we introduce “municipal entrepreneurship” as a rich new area for entrepreneurship scholars. We present a number of examples that illustrate how urban centers, as they continue to expand with often-accompanying increases in population density, experience common “growth problems”. We believe that the role of municipal governments as engines of economic growth and their importance for facilitating innovation and entrepreneurship in these growing cities has been under-researched. Specifically, this paper addresses the lack of research on the potential impact of municipal demand-side instruments. In this context, we extend the existing research by presenting a Procurement Continuum that ranges from “bureaucratic procurement” to “procurement for innovation”, and introduces “entrepreneurial procurement” as a new innovative municipal tool to create opportunities for new goods and services. We further articulate five procurement premises and offer a research agenda emerging from our premises and conceptual development as first steps toward developing propositions and testable hypotheses regarding demand-side instruments for municipal entrepreneurship.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".