Bibliographic record
Abstract
Purpose The purpose of this paper is to illustrate experimentation over time in Ontario, Canada with place-based innovation policies to support the development and coordination of entrepreneurial ecosystems on a regional basis across the province. Design/methodology/approach Tracing the policy learning process and successive adaptations in program design over time, the authors provide a detailed case study of the evolution of the Ontario Network of Entrepreneurs (ONE) from 2003 to the present. Findings The authors find that the program has evolved in response to regular program reviews that include broad input from ecosystem actors operating at multiple levels within the network, and that intermediaries are key facilitators of inter- and intra-ecosystem linkages. However, program complexity and coordination challenges suggest that place-based innovation policies, such as the ONE, should focus specifically on innovation-intensive entrepreneurship. Research limitations/implications These findings make three contributions to the theory and practice of place-based innovation policy. First, these policies are by nature experimental because they must be able to flexibly adapt according to policy learning and practitioner input from a wide variety of local contexts. Second, multilevel interactions between provincial policymakers and regional ecosystem actors indicate that place-based innovation policy is neither entirely driven by “top down” policy, nor “bottom up” networks but is rather a complex and variable “hybrid” blend of the two. Finally, publicly funded intermediaries perform essential inter- and intra-ecosystem connective functions but system fragmentation and “mission creep” remain enduring policy challenges. Originality/value The paper makes an original contribution to the literature by analyzing the development of entrepreneurial policy support framework and situating the case study in the context of the policy learning process involved in place-based innovation policymaking in North America.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".