Bibliographic record
Abstract
The presence of innovative clusters may positively influence domestic economic growth. If it is possible to identify these clusters, economic growth may be increased even further by developing policy that stimulates innovative clusters. Identifying clusters, however, is not an easy task. Input- output tables provide a possibility to identify clusters of sectors. These so-called meso clusters answer the question as to which sectors firms that work together stem from. Hence, meso-clusters provide a framework which indicates how a cluster may be composed. An earlier analysis identified Dutch meso clusters. This opens the possibility to compare these Dutch clusters with clusters in other countries, which helps to answer two questions. Firstly, it shows which clusters exist in which countries. These clusters highlight difference in the way sectors work together in different countries, which may explain different patterns of specialisation or even different economic growth rates. Secondly, differences between similar clusters in different countries are analysed. These differences may stem from, for example, different sectors in similar clusters or from different levels of innovation, productivity, profitability or different export positions of similar clusters. The present paper uses the cluster identification method to compare national clusters in ten countries: Australia, Canada, Denmark, France, Germany Italy, Japan, The Netherlands, The United Kingdom, and The United States. The most remarkable differences between clusters found in these countries and the most interesting differences between the sectors included in the same cluster in different countries are discussed. This provides insight in the differences of the inter-industry linkages in these countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".