The impact of the EU Industrial R&D Investment Scoreboard on science and policy
Bibliographic record
Abstract
Abstract The EU Industrial research and development (R&D) Investment Scoreboard (Scoreboard) provides data and economic analysis to monitor corporate R&D and inform EU policy since 2004. This study investigates the influence of this annual report on both science and policy. Our findings reveal that while the Scoreboard has been more frequently cited in policy documents than in peer-reviewed papers, academic interest is growing. In policy, it has influenced the EU policy narrative regarding the EU corporate R&D intensity gap relative to its competitors. In science, citations are more often linked to specific analytical insights of the reports than to the underlying data. However, studies combining Scoreboard and patent data receive relatively more citations, highlighting the value of integrating diverse data to better understand innovation dynamics. Interestingly, policy documents citing the Scoreboard exhibit a shorter citation time window than academic papers, reflecting its immediate relevance to policy debates.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".