Clusters from the Inside and Out: Local Dynamics and Global Linkages
Bibliographic record
Abstract
This paper surveys some of the current methodologies employed to analyse cluster development, as well as some of the key themes emerging from both the analytical and prescriptive literature noted above. It uses this survey as the context in which to present a synthesis of the initial findings of the current national study of industrial clusters in Canada, conducted by the Innovation Systems Research Network. The national study comprises 26 cases which aim to identify the presence of significant concentrations of firms in the local economy and to understand the process by which these regional-industrial concentrations of economic activity are managing the transition to more knowledge-intensive forms of production. The central questions in each case are: What role do local institutions and actors play in fostering this transition? How important is interaction with non-local actors in this process? How dependent are local firms on unique local knowledge assets and what is the relative importance of local versus non-local knowledge flows between economic actors? How did each local industrial concentration evolve over time to reach its present state and what key events and decisions shaped its path? And, finally, to what extent do these processes, relationships and local capabilities constitute a true cluster? Ultimately, what are the key relationships, linkages and processes that ground the cluster in its existing location?
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".