Bibliographic record
Abstract
Traditional Chinese Medicine (TCM) is widely practiced among people from East Asia; but using TCM is often seem as being in opposition to Western medicine. When does one use TCM, and when does that same person use Western medicine? The most difficult part of this question is the fact that TCM encompasses so many facets of one’s life – it’s not just about being sick, but more about being healthy in general. It also creates a lot of identity clashes, and for many Asian Canadians, it brings up issues of the Model Minority Myth and having to choose one culture to abide by. And how do these things get wrapped up in racism? And does it have to be a zero-sum choice? Listen to Chen and Fu as they unpack their own experiences. They also interview Andre Shih, a TCM Herbalist and Accupuncturist operating out of Vancouver, who answers questions about the challenges of being a TCM practitioner in Canada. Ultimately, knowing how important TCM is to vast numbers of diaspora in Canada, how can we re-envision the landscape of healthcare regarding TCM’s relationship with 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".