Dissemination of Traditional Chinese Medicine in Latin America and the Caribbean: the Cases of Peru, Chile, and Cuba
Bibliographic record
Abstract
Traditional Chinese medicine (TCM) arrived from China to Latin America and the Caribbean in the 1840s due to the massive migration of Chinese people to the region. In a few years, the press noticed the presence of Chinese herbalists practicing in different cities and countries regardless of the demographic weight of the Chinese community. The fascination with Chinese doctors implicated not only the press but also the literature, a phenomenon particularly observed in Cuba. In the first decades of the 20th century, the reactivation of Chinese immigration to the region fostered an anti-Chinese climate that materialized in more significant migratory restrictions and control of their businesses, such as what happened with Chinese herbalists. These herbalists who practiced inside and outside the Chinese community started to object to criticism and persecution by the conservative press and professional doctors. Despite this, Chinese doctors will continue to maintain their support of a significant number of ill persons. This work seeks to illuminate the historical relevance of TCM in Latin America and the Caribbean, focusing on the cases of Peru, Chile, and Cuba. This last country was far from China culturally and geographically, but as in many other small towns in the region, Chinese medicine presented an alternative to the treatment of illnesses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".