The Quadruple Role of Henry C. Lu in the International Communication of Traditional Chinese Medicine
Bibliographic record
Abstract
Based on the life of Dr. Henry C. Lu as a practitioner of the Traditional Chinese Medicine (TCM hereafter) and his many books on TCM practice and education, this article draws his profile to show his endeavors in the international communication of TCM as a practitioner, scholar, writer, and educator. He earned worldwide fame from A Complete Translation of the Yellow Emperor’s Classic of Internal Medicine and the Difficult Classic, one of the representative English translations of Huangdi Neijing. We summarized the gap between TCM and Western medicine by use of the Acro Diagram or Tai Chi Diagram, Dr. Lu found a clever way to code syndromes and rank them by voting to jump over this gap. Several key concepts in TCM is re-explained and new words are coined by us to convey their exact meaning. Dr. Lu’s comprehensive list of coded syndromes can help TCM practitioners make an objective diagnosis easily, which we explain by the set theory. In 1986, Dr. Lu founded the International College of Traditional Chinese Medicine of Vancouver which was renamed as Tzu Chi International College of Traditional Chinese Medicine in 2015. Together with his Canadian-Chinese colleagues, Dr. Lu was one of the pioneers to strive over decades for the legislation of TCM, making British Columbia the first region in North America to recognize TCM as a medical major with four types of registerable titles equivalent to Western medicine.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".