Transformative Capabilities in the “Very Old Economy”: Intersectoral Innovation Networks and Learning Alliances
Bibliographic record
Abstract
The central starting point of the paper is that well-known classical patterns of creating innovations are changing. Innovation processes aren’t originated exclusively within ‘knowledge intensive’ (high-tech) companies any more, but rather mutually between companies of different sectors. This has twofold consequences: On the one hand, the so-called high-tech industry influences non-high-tech branches as well as important suppliers of innovative solutions. On the other hand, the particular requirements and conditions of so-called low-tech branches affect companies of high-tech industries as specific drivers of innovation, too. Considered as a productive force, intersectoral cooperation becomes a main source of innovation, further growing in importance; innovation processes are more and more organized along “distributed knowledge bases” across economic sectors.Therefore, the main thesis of the paper is that today´s knowledge isn’t located within one company originating from one industrial sector any more, but is distributed along the respective value-chain and various sectors. At the same time, the “network” is not only relevant in technology-intensive branches such as IT industry or automotive industry, but becomes also a relevant analytical category for exploring “low-tech” industries and their innovation processes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".