Analysis of the World Top 1000 Corporations RaD Investment in 2014: With Focus on those Chinese Corporations
Bibliographic record
Abstract
Each year, the European Union's (EU) Economics of Industrial Research & Innovation (IRI) project group will publish a report entitled "R&D Scoreboard" which summarizes the world top 1,500 to 2,500 corporations (number varies from year to year) R&D investment in the previous year.In this paper, the author will extract the top 1000 corporations from this EU 2015 report, then analyze and compare the Chinese corporations' performance versus the rest of the pool.By comparing the investment in different sectors, we found that the Chinese corporations' primary R&D investment, similar to other regions, were in the construction, IT industry, transportation, and energy sectors.However, different from other regions, Chinese R&D investment in the pharmaceuticals and biotechnology is significantly less than the rest of the world.Our next step is to compare the annual profit margin with the R&D investment percentages of these Chinese corporations.The objective is to verify whether the Chinese corporations will stick to the sustainable strategy of continuously increasing in R&D investment, regardless of the short-term revenue fluctuations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".