Daoism in Brazil: The Globalization of the Orthodox Unity (Zhengyi) Tradition
Bibliographic record
Abstract
The Daoist Society of Brazil (Sociedade Taoista do Brasil) was founded by Wu Jyh Cherng (Wu \nZhicheng 武志成), a Taiwanese immigrant, in 1991. Today the Society operates two temples in \nRio de Janeiro and São Paulo, and is the only religious organization in South America that is \naffiliated with the Daoist Society of China in the Orthodox Unity (Zhengyi 正一) lineage. The \ngroup, however, is not made up of other immigrants or Brazilians of Chinese descent; rather the \nentire group and current priesthood consist of Brazilians of non-Chinese ancestry. \nThis essay examines the history and development of the Daoist Society of Brazil and its \nmembers’ understandings of Daoism through site visits and interviews conducted in December \n2009. From this I develop a theory of the particular characteristics of Brazilian religious culture \nthat enabled the formation of a Zhengyi Daoist community outside of ethnically Chinese people. \nThe practices of the two temples thus represent a hybrid of Chinese and Brazilian cultural forms \nthat have been negotiated through a complex assessment of how to maintain authentic religious \ntraditions in a non-traditional cultural context. Although the transcultural practices of the Daoist \nSociety of Brazil can only be fully understood in these particular terms, the Society also \nrepresents an important case study in the more general phenomenon of the globalization of \nreligious cultures and the formation of transcultural religious identities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".