Spillovers and Risk of R&D Projects, and Targeting of Public R&D Support (Japanese)
Bibliographic record
Abstract
This paper analyzes what types of R&D projects and R&D firms generate important spillover effects, and which are subject to funding constraints. It also analyzes what types of projects and firms are targeted for public support and whether the selection of targets is consistent with spillovers. Using data collected in an inventor survey conducted by the Research Institute of Economy, Trade and Industry and those in the Basic Survey of Business Activities by the Ministry of Economy, Trade and Industry, we focus primarily on corporate research projects. Major findings are as follows: Corporate research projects generate substantial spillovers. For example, about 20% of such projects involve basic research. At the same time, however, about 10% of corporate research projects have been delayed or scaled down due to funding constraints, and one quarter of them have faced constraints in funding commercialization investments. It has been also found that only 3% of Japanese corporate R&D projects receive public support, and 5.6% of the financially constrained projects receive such support. Our statistical analysis suggests that although the estimated conditions explaining public support for R&D are largely consistent with the estimated conditions explaining the spillovers as measured by the publication of scientific papers and the occurrence of serendipity, there are some gaps. Although a firm's highly intensive R&D and participation of a PhD inventor are estimated to be significant sources of spillovers, apparently they are not given high weights in terms of factors related to public support for R&D.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".