‘Islands of Innovation’ and diversities of innovation in the UK and France
Bibliographic record
Abstract
This paper explores diverging patterns of innovation and regional development in two ‘islands of innovation’. In the early 2000s the growth trajectories of Grenoble and Oxfordshire were compared (Lawton Smith 2003). The focus was on national laboratories as territorial actors in the clustering of high-tech firms. Building on longitudinal data collected since 2003 the theme shifts in this study to the forms that government intervention takes through investments in knowledge organisations in high tech economies and how that leads to particular specialisations of technological advance. While there are many similarities, there are differences in starting points and structures, leading to diversities in innovation. The analysis shows how both are embedded in their national situations and opportunities for development. We focus on two key elements in sustaining clusters of innovation, those of highly skilled labour and networks. We show that in Grenoble, the clusters are orchestrated information and project-based while in Oxfordshire they are labour market dominated and organic. We demonstrate complementary relationships between the national and regional level policy formation and implementation. In both cases importance of place is sustained over time but for different reasons.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".