Bibliographic record
Abstract
The “science park” model has long been showing signs of aging, with many science parks now facing budget cuts by local and regional governments. In this study, we dissect the blueprint of a highly successful campus-based ecosystem, the High Tech Campus Eindhoven (HTCE). As an innovation ecosystem, the HTCE provides its residents (a) access to shared resources and facilities, to facilitate research and product development, and (b) an innovation community that enhances knowledge sharing between people at the campus. The success of the HTCE arises from a deep and inclusive understanding of the conditions in which an ecosystem for research and development can thrive, and the commitment to carefully grow and sustain these conditions. These conditions include: low physical distances between the various buildings, offices and shared facilities; a dynamic portfolio of thematic workshops and meetings stimulate knowledge sharing and informal networking; careful management of the diversity and reputation of the campus; attracting and hosting “connectors” that have the capability to initiate and/or manage collaboration across a newly emerging value chain; and a high level of responsiveness to requests and feedback of residents.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.035 | 0.020 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".