Bibliographic record
Abstract
In this book, a large and diverse group of researchers pool their knowledge to take the measure of China's massive, protracted, and unexpected economic upsurge, which began in the late 1970s and continues as this is written nearly thirty years later. The magnitude and rapidity of China's recent gains stand out even against the background of stunning growth among China's East Asian neighbors during the late twentieth century. China's extended boom began at remarkably low levels of income and consumption. Its growth spurt is remarkable for its geographic spread as well as its speed and longevity. While coastal regions have led the upward march of output, exports, and income, China's central and western regions have recorded enormous gains as well. A brief summary can delineate the magnitude of China's recent economic achievements. One careful review of available data finds that average gross domestic product (GDP) growth increased from approximately 4 percent prior to the reform to 9.5 percent during 1978–2005 (see Chapter 20). Although Young (2003) and others label recent growth as extensive, meaning that the main motive force comes from adding more labor and capital to the production process, the same study finds that productivity improvement accelerated from 0.5 to 3.8 percent per annum after the reform, with productivity change accounting for 40.1 percent of overall GDP growth during 1978–2005, as opposed to 11.4 percent during 1952–1978 and –13.4 percent (i.e., a productivity decline) during 1957–1978 (see Table 20.2).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".