Bibliographic record
Abstract
Regional Development in the Knowledge Economy, Philip Cooke and Andrea Piccaluga (eds), London and New York, Routledge, 2006, 304 pp., £65.00 (h/b only) Regional Development in the Knowledge Economy is a thirteen-chapter text exploring the manifestations of the knowledge economy, originating from the Regional Studies Association conference 'Reinventing Regions in the Global Economy' (2003). An introduction by Cooke sets the context, discussing what he sees as a move from a globalisation of consumption ('Globalisation 1') to a globalisation in which multinational corporations increasingly practise open innovation ('Globalisation 2'). Chapter 2, by Caniels and Romijn, addresses the role played by local knowledge spillovers (LKS) in innovation. Exploring firm-region (micro-meso) interaction, the authors conclude that LKS research has too readily accepted knowledge spillovers as the universal driving force of regional innovation and growth. 'Knowledge-Intensive Industries and Regional Development' by Isaksen analyses the spatial bias of knowledge intensive industries to large and university towns in Norway. Isaksen concludes that firms in mediumsized cities cooperate locally the least and that informal networks, consultants and ICT solutions are more common in the capital. Continuing the theme of spatial patterns and software firms, Weterings and Boschma's chapter looks at the spatial pattern of newly emerging industries using 265 Dutch software firms. At times this chapter lacks depth, for example concluding that software firms perform better when the founder has worked in the sector. 'Constructing Regional Advantage at the Northern Edge' by Coenen and Asheim compares three contrasting Regional Innovation Systems (RIS), concluding with case studies (clusters in Denmark, Canada and Sweden) that show that the successful construction of an RIS takes place within a dynamic, broad and active triple-helix. Studying three biotech clusters in the USA, Renko's chapter shows that small biotech firms source market knowledge from a variety of stakeholders and that geographybased differences in sourcing market knowledge are virtually non-existent. Continuing the biotechnology theme, 'Knowledge Access at Distance' by Fontes is concerned with addressing what is happening outside major biotech agglomerations. Using Portuguese NBFs, Fontes concludes that the co-location of firms is not crucial to success as long as firms create 'alternative forms of proximity'. While this chapter is insightful, the poor English renders it difficult to digest. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".