Role of regional cluster development case study : supporting virtual enterprises (VE)
Bibliographic record
Abstract
Clusters in general are a particularly important way through which location-based complementarities are realized.This paper shows one example of regional cluster composition in the economic performance of industries, clusters and regions in the field of the Telecommunication sector in Germany.It examines the role of regional clusters in regional entrepreneurship.We focus on the distinct influences of convergence and agglomeration on growth in the number of start-up firms as well as in employment in these new firms in a given region of a special industrial sector.The first step in the lifecycle of a virtual enterprise is the identification of potential companies or company departments which have a common business goal.In order to approach the seed identification problem of virtual companies, three basic sub-problems have to be solved.Firstly, relevant company data and information have to be acquired.Secondly, the information has to be analysed in order to find common aspects and business goals.Thirdly, selection criteria have to be defined in order to decide whether a company might be part of the virtual enterprise or not.The paper at hand presents an approach to semi-automate the seed identification of the Mobile Communication Cluster.Its use inside a company gives rise to the discovery of new business opportunities through automated business segment analysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".