Intimate Communities: Wartime Healthcare and the Birth of Modern China, 1937–1945
Bibliographic record
Abstract
When China’s War of Resistance against Japan began in July 1937, it sparked an immediate health crisis throughout China. In the end, China not only survived the war but emerged from the trauma with a more cohesive population. Intimate Communities argues that women who worked as military and civilian nurses, doctors, and midwives during this turbulent period built the national community, one relationship at a time. In a country with a majority illiterate, agricultural population that could not relate to urban elites’ conceptualization of nationalism, these women used their work of healing to create emotional bonds with soldiers and civilians from across the country. These bonds transcended the divides of social class, region, gender, and language. “Nicole Elizabeth Barnes demonstrates remarkable insights into some of the most well-known figures in healthcare in wartime China—and introduces many previously unknown—providing pointed character analyses while also connecting individual experiences to larger sociopolitical trends across the tumultuous wartime landscape.” SONYA GRYPMA, PhD, RN, author of China Interrupted: Japanese Internment and the Reshaping of a Canadian Missionary Community “Not only a major contribution to the histories of medicine, gender, emotion, and nationalism, but even more importantly, it opens up exciting horizons by making visible and exploring the surprising entanglements between them all.” SEAN HSIANG-LIN LEI, author of Neither Donkey nor Horse: Medicine in the Struggle over China’s Modernity NICOLE ELIZABETH BARNES is Andrew W. Mellon Assistant Professor of History and Gender, Sexuality and Feminist Studies at Duke University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| 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".