Bibliographic record
Abstract
The new structural economics is an approach to development theory associated with former World Bank chief economist Justin Yifu Lin and his colleagues at Peking University. Its practitioners understand themselves as developing and applying a “third‐generation” paradigm for development economics, avoiding the errors of first‐generation structuralism, associated with import‐substitution industrialization policies, and second‐generation neoliberalism, associated with the Washington Consensus program of market‐oriented institutional reform. Influenced by China's development experience, this theory conceptualizes national economies as belonging to a spectrum of different levels of development defined by their relative factor endowments (“industrial structure”), from a low‐income agrarian economy to a high‐income industrialized economy; it insists that an optimal development strategy follows from specialization according to a country's comparative advantage; and it explicates a role for the government in facilitating the graduation from one stage to another through industrial upgrading and infrastructure improvements. Though the new structural economics aligns with post‐neoliberal revisionism in Western development economics, it is gaining a particular institutional presence in China through a network of think‐tanks, development assistance centers, and university partnerships with China's rural provinces and cities. Geographers have as much to learn about this approach to development theory as they do about its practice.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".