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
This publication explores the success of major innovation and entrepreneurship clusters in OECD countries, the challenges they now face in sustaining their positions and the lessons for other places seeking to build successful clusters. What are the key factors for cluster success? What problems are emerging on the horizon? Which is the appropriate role of the public sector in supporting the expansion of  clusters and overcoming the obstacles? The book addresses these and other issues, analysing seven internationally reputed clusters in depth: Grenoble in France, Vienna in Austria, Waterloo in Canada, Dunedin in New Zealand, Medicon Valley in Scandinavia, Oxfordshire in the United Kingdom, and Madison, Wisconsin, in the United States. For each cluster, it looks at the factors that have contributed to its growth, the impact of the cluster on local entrepreneurship performance, and the challenges faced for further expansion.  It also puts forward a set of policy recommendations geared to the broader context of cluster development. This publication is essential reading for policy makers, practitioners and academics wishing to obtain good practices in cluster development and guidance on how  to enhance the economic impact of clusters. Â
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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