Bibliographic record
Abstract
We view development economics as a set of empirical generalizations, paradigms, and tools that tell us something about why large differences in productivity and income within and among countries, social groups, and classes seem to persist. How does China fit into the received views of development economics? In answering this question the cup overfloweth with materials, so no attempt is made to be exhaustive. We begin in Part A by using comparative statistics of developing and developed countries to help understand China's present position and its growth experience since 1978. Ensuing sections take up selected topics where development economics and China's development experience intersect. Part B looks at governance issues in a comparative framework, focusing on corruption as a phenomenon that brings together many features of administration that are common across countries. Part C examines the rural sector, focusing on agricultural productivity and land tenure, and Part D reviews the uniquely East Asian phenomenon of rural industrialization. Part E concludes. CHINA'S COMPARATIVE ECONOMIC PERFORMANCE: A QUANTITATIVE LOOK How do China's economic performance and structure compare to those of other developing countries? In this section we situate China within the developing world, using comparative measures that are in some cases straightforward and in others somewhat controversial. Jan Svejnar's contribution (see Chapter 3) to this volume contains tables allowing similar comparisons with the transition economies. Variables examined include demographic, environmental, and energy indicators, economic data from national income and product accounts, Gini coefficients and related distributional measures, and selected human capital information.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".