Scientific Sources of Corporate Inventions in Japan: Evidence from an inventor survey (Japanese)
Bibliographic record
Abstract
We conducted an inventor survey to examine the contribution of science to corporate inventions. The survey results show that for about one-quarter of the inventions, scientific knowledge embodied in literature, equipment, or research materials in the last 15 years was essential to conceive or implement research and development (R&D). If it were not for the collaboration with universities, 3% of the R&D projects would not have been implemented. In total, for two-thirds of the inventions, scientific knowledge contributed to implementing and accelerating R&D. These results indicate the importance of scientific knowledge as a public good to promote corporate inventions. We also found that about 70% of the scientific knowledge source of Japanese inventions was generated in Japan: the suppliers of scientific sources were located domestically. If the scientific sources are cited in the patent document, they are more likely to be cited at where the prior art is described rather than where the invention is described. Moreover, the results show that only 15% of the inventions with important scientific sources cite such important literature in the patent document, and only 16% of the inventions citing non patent literature actually cite the important scientific sources. This result means that the patent citation is an incomplete and noisy index to trace the knowledge flow.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".