The 2006 EU Survey on R&D Investment Business Trends
Bibliographic record
Abstract
This report presents the findings of the sixth survey on trends in business R&D investment. These are based on 205 responses of mainly larger companies from the 1000 EU-based companies in the 2010 EU Industrial R&D Investment Scoreboard. These 205 companies are responsible for R&D investment worth almost €40 billion, constituting around 30% of the total R&D investment by the 1000 EU Scoreboard companies.\nThe main result is that top R&D investing companies in the EU expect their global R&D investments to grow by 5 % annually from 2011 to 2013. This is more than double the rate of last year's expectations, and represents a significant upturn from the 2.6 % R&D investment cuts observed for these companies in 2009. Companies surveyed expect their R&D investment inside the EU to grow 3 % a year over the next three years, although this remains the lowest rate compared to what they expect to invest in R&D in other world regions, especially in Asian countries like China (25%) or India (8%), but also in the US and Canada (5%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.025 |
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; both teacher heads agree on what is shown here.
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".