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Record W4387900891 · doi:10.1097/mc9.0000000000000083

Dissemination of Traditional Chinese Medicine in Latin America and the Caribbean: the Cases of Peru, Chile, and Cuba

2023· article· en· W4387900891 on OpenAlexaff
Patricia Palma

Bibliographic record

VenueChinese Medicine and Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsChinaLatin AmericansPersecutionImmigrationEthnic groupTraditional medicineGeographyPolitical scienceHistoryEthnologyDevelopment economicsEconomic growthMedicineLawPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.317
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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