Bibliographic record
Abstract
A practical handbook for accelerating innovation, both internally and externally, through engagement with innovation ecosystems. Leaders in large organizations face continuous pressure to innovate, and few possess all the internal resources needed to keep up with rapid advances in innovation, science, and technology. But looking beyond their own organizations, most face a bewildering landscape of external resources. In Accelerating Innovation, these leaders—whether from the private, public, or nonprofit sectors—will find a practical guide to this external landscape. Authors Phil Budden and Fiona Murray provide directions for navigating innovation ecosystems—those hotspots worldwide where researchers, entrepreneurs, and investors congregate. While Silicon Valley and Greater Boston are popularly known for web-based digital technology and biotechnology respectively, the logic of innovation ecosystems is not solely American—so this guide takes in new locations and varied sectors such as Singapore (smart cities), Perth (mining), Cairo and Dubai (fintech), London and Lagos (fintech and media), Copenhagen (quantum computing), Rio de Janeiro (energy), Halifax (oceans), and Tel Aviv (cybersecurity). Drawing practical advice from a synthesis of works on tech, innovation, entrepreneurship, and strategic management, and from a decade of their own research and teaching at the intersection of these topics, Budden and Murray distill insights and interconnections from all these different worlds into a useful and globally applicable set of frameworks and models. Their approach provides leaders at every organizational level with a clear and workable roadmap for making the most of the unique resources of innovation ecosystems, and how to bring that into their organizations.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.150 | 0.106 |
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".