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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".