Bibliographic record
Abstract
Scholars in University of Quebec, Sabourin & Pinsonneault（1997）, in RD meanwhile, a cause-effect relationship of consecutive path has been found within these four elements. Industrial cluster is the drive for enterprises to generate innovative abilities, the basic of national competitiveness, an essential link of National Innovative System (NIS). Through the establishment of science parks, a nation-state is able to foster the development of its high tech industry and then invite leading companies and up and down stream suppliers. Because of value-chain cluster, the interdependent companies create industrial cluster relationship and effects and companies based on this innovative base will be able to enhance its competitiveness. This research found that strategic resources of industrial cluster (high quality human resources, knowledge, technical base, capital resources, and infrastructure) indeed help the development of industrial cluster. There is also a significant relationship shown in industrial cluster relationship between enterprises (cooperation and competition) and the generation of effects (geographic concentration, integration of up to down stream suppli- ers, horizontal competition, vertical cooperation and resource sharing). At the same time, companies will create innovative advantages in response to demands of new products, new services, new industries, and new organizations due to innovative milieu and further bring prosperity to overall economy. This has been proven by the development of diversified industries in Silicon Valley, integrated circuit industry in Hsinchu Science Park, and Photonic Industry in Southern Taiwan Science Park. In short, it is also the purpose of this research to invite more participation in the study of strategic resources of industrial cluster from the industrial, governmental, and academic circles to promote and maintain sustainable development of industries in the future and to keep up with science parks around the world in the regards of management efficiency and R&D innovative results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".