Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Views and News of the Current Research-Technology Management Scene Whether one calls it a polycentric R&D strategy, a multinodal approach, or something else altogether, recent studies have made it clear: the globalization of R&D has begun. a 2010 Ernst & Young survey of 1,050 company executives, U.S. companies indicated that they will increasingly shift more R&D dollars and capacity to places like China, India, and Brazil. Currently, according to the Ernst & Young survey, fewer than 11 percent of the U.S. companies surveyed said they currently spend more than 25 percent of their R&D budget in emerging markets. But by 2015, the number of companies at that level of investment is projected to double. speed of the trend is even more dramatic for Western European companies, which may see a tripling of the number of companies with at least a quarter of their R&D investment in emerging markets (from 6.9 percent today to 18.8 percent in fi ve years, according to the E&Y report). Firsthand observers of the trend, such as Vivek Wadhwa, a Duke University business school professor, say it's hard to overplay its signifi cance. The United States needs to wake up and realize that the world has changed, says Wadhwa. Top companies like IBM, GE, and HP now get the majority of their revenue from abroad, and they need to be close to their markets. This approach makes the most sense for them. Like Wadhwa, University of Maryland professor Anil Gupta has been studying and writing about emerging markets for years. author of Getting China and India Right says there are a few key reasons why Western companies are decentralizing their R&D at an increasing rate. First, many Western-designed products need to be redesigned to lower costs if they are to be successful with the growing middle class who represent the biggest increase in purchasing power in these new markets. Emerging markets are becoming bigger quite rapidly. But many products are designed for richer customers, not the middle income customer. When companies relocate R&D to an emerging market, they challenge themselves to create lower-cost solutions to meet the needs of consumers in that market. Sometimes developing lower-cost alternatives for emerging markets brings benefits back home, as well. India, General Electric (whose 5,000-person R&D center in Bangalore is bigger than GE's largest R&D center in the United States) developed a portable ECG machine that costs a fi fth as much as the baseline U.S. model. Now it's selling the device in niche markets in the United States. In the future, we will see more innovations being developed abroad and brought to the United States, says Gupta. Whether or not a given product yields such a reverse innovation effect, having R&D capacity close to the new market is essential if the product is to hit its mark. A second key driver of the move to overseas R&D: dollar for dollar, higher returns on the R&D investment. China and India, for example, market-rate compensation for scientists and engineers is about a quarter what it is in the United States. Venture capitalists and corporations alike see their R&D investment stretching farther overseas. That's a good sell right now, especially for industries like, for instance, pharmaceuticals, in which only a small percentage of trialed products make it to market. Lower costs allow these companies to hedge their bets more effectively as they search for the rare product that really takes off. And, the sheer number of scientists and engineers being produced by universities in emerging markets allows a quicker scaling up of R&D. This is true even when the quality factor is weighed, argues Gupta. While the average researcher in the United States is better trained than the average researcher in an emerging market, there are enough scientists in the top stratum of foreign researchers that companies can draw from. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 it