Looking forward via the Past: An Investigation of the Evolution of the Knowledge Base of Robotics Firms
Bibliographic record
Abstract
The case studies described in this paper investigate the evolution of the knowledge bases of the two leading EU robotics firms - KUKA and COMAU. The analysis adopts an evolutionary perspective and a systems approach to examine a set of derived patent-based measures to explore firm behavior in technological knowledge search and accumulation. The investigation is supplemented by analyses of the firms' historical archives, firm strategies and prevailing economic context at selected periods. Our findings suggest that while these enterprises maintain an outwardlooking innovation propensity and a diversified knowledge base they tend to have a higher preference for continuity and stability of their existing technical knowledge sets. The two companies studied exhibit partially different responses to the common and on-going broader change in the robotics industry (i.e. the emergence of artificial intelligence and ICT for application to robotics); KUKA is shown to be more outward-looking than COMAU. Internal restructuring, economic shocks and firm specificities are found to be stronger catalysts of change than external technology-based stimuli.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".