Bibliographic record
Abstract
While global competitiveness is increasingly invoked as necessary for economic success stories, there are few answers available about how it can be achieved or maintained. The idea of stimulating industries to spur on economies is often proposed, but industrial policy can be seen as a boondoggle of government spending, and theorists of globalization are doubtful that such efforts can succeed in a world of fragmented supply chains. What Makes Clusters Competitive? tests fundamental theoretical hypotheses about what makes industries competitive in a globalized world by using the wine industries of several countries as case studies: Extremadura (Spain), Tuscany (Italy), South Australia, Chile, and British Columbia (Canada), Taking into account historical and location-specific characteristics, and drawing out policy lessons for other regions that would like to promote their industries, this volume demonstrates the value of applying cluster theory to understand market forces, while also describing the forces underlying the development of the wine industry in a range of different settings. An excellent resource for those interested in what makes industries succeed or struggle, What Makes Clusters Competitive? offers guidance for policymakers and the private sector on how to promote local industries. Contributors include David Aylward, Alexis Bwenge, Sara Daniele, F.J. Mesías Díaz, Christian Felzenstein, Husam Gabreldar, F. Pulido García, Sarah Giest, Elisa Giuliani, Andy Hira, Mike Howlett, A.F. Pulido Moreno, and Oriana Perrone.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".