The 2014 EU Industrial R&D Investment Scoreboard
Bibliographic record
Abstract
The 2014 "EU Industrial R&D Investment Scoreboard" (the Scoreboard) contains economic and financial data for the world's top 2500 companies ranked by their investments in Research and Development (R&D). The sample contains 633 companies based in the EU and 1867 companies based elsewhere. The Scoreboard data are drawn from the latest available companies' accounts, i.e. usually the fiscal year 2013/14. \nKey findings of the 2014 Scoreboard comprise: \n- The world top 2500 R&D investors continued to increase their investment in R&D (4.9%), well above the growth of net sales (2.7%). The 633 EU companies increased R&D by 2.6% and decreased sales by 1.9%. \n- Volkswagen leads the global ranking for the second consecutive year, showing again a remarkable increase of R&D (23.4%, up to €11.7bn). Second continues to be Samsung, showing also an impressive R&D increase of 25.4%. \n- EU companies in the automobile sector, accounting for one quarter of the total EU’s R&D, continued to increase significantly their R&D (6.2%). This reflects the good performance of automobiles companies based in Germany (9.7%) that account for three quarters of this sector’s R&D in the EU. \n- The poor R&D performance of EU companies in high-tech sectors such as Pharmaceuticals (0.9%) and Technology Hardware and equipment (-5.4%) weighed down the total R&D increase of the EU sample. The overall amount invested in R&D by EU companies in high-tech sectors represents 40% of the amount invested by their US counterparts and the gap between the two company samples is increasing with time.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".