Bibliographic record
Abstract
Using the author’s recently constructed data set, this article measures the productivity performance of China’s 19 manufacturing industries, four mining industries, plus utilities, over the reform period 1980-2005. The approach is based on neoclassical assumptions on institutional settings and behavior of agents. Some of these assumptions are questionable in the case of China, but the results can be used as a starting point for further investigation. We find that the post-reform industrial growth in China had been largely investment-driven and inefficient until the 2000-05 period. Following China’s accession to WTO in 2001, Chinese industry experienced the best performance in TFP, accounting for 50 per cent of the growth of industrial value added. However, the mining sector had been most inefficient and had not yet shown a clear sign of improvement by 2005. Traditional labour intensive manufacturing did not appear to be efficient as suggested by the theory of comparative advantage, but there was a sign of significant improvement in 2000-05. By contrast, the capital and technology-intensive industries engaged in consumer goods manufacturing were most efficient throughout the entire period, apparently due to continuous foreign direct investment, high exposure to international competition and less state intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".