Kate Merkel-Hess. The Rural Modern: Reconstructing the Self and State in Republican China.
Bibliographic record
Abstract
The word “modern” has several connotations for China. Often “modern” means the creation of cities, notably Shanghai, that replicate Western infrastructure, architecture, and institutions. Sometimes “modern” means new Chinese art and literature based on Western models. Sometimes “modern” means imported Western political ideologies, such as democracy or Marxism. In The Rural Modern: Reconstructing the Self and State in Republican China, Kate Merkel-Hess looks at a quite different “modern,” the rural modern, embodied in projects in Republican China that aimed to transform the lives of 85 percent of China’s population, the peasants. These projects were designed to remake China from the bottom up through “creating resilient, sustainable rural communities” (114). Sadly, this “modern” fits Alexander Woodside’s elegiac “lost modern” (Lost Modernities: China, Vietnam, Korea, and the Hazards of World History [2006]): what might have been. The projects were doomed by war; they came to an end in the chaos and violence of Japanese invasion in 1937. They must still be seen as courageous and creative efforts that might have created a new world. In her penetrating analysis in this excellent monograph, Merkel-Hess has written a detailed description of the projects—and an homage to the men who spearheaded them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".