Bibliographic record
Abstract
Beijing today looks much like Chicago, Toronto, Brussels, or any other modern city – high-rise office buildings clad in glass, streets packed with automobiles and trucks, and billboards, many of them in English, advertising name-brand products, many of them U.S. name brands, though that, of course, does not necessarily mean they were manufactured in the United States. The same can be said of Shanghai, Nanjing, and a number of other Chinese cities. The U.S presence in China, which is apparent even to the most casual observer, is growing rapidly. From 1993 to 2002, direct investment in China by U.S. companies increased by a factor of ten, making the United States the second largest foreign investor in China. (Hong Kong, though nominally a part of China but with a separate economic and political structure, is the largest foreign investor.) A substantial portion of the U.S. investment (approximately 60 percent) is in the manufacturing sector, which turns out vast quantities of merchandise, some of it sold in the rapidly expanding Chinese market, some of it exported for sale in the United States and elsewhere.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".