State risk discourse and the regulatory preservation of traditional medicine knowledge: The case of acupuncture in Ontario, Canada
Bibliographic record
Abstract
Several United Nations bodies have advised countries to actively preserve Traditional Medicine (TM) knowledge and prevent its misappropriation in regulatory structures. To help advance decision-making around this complex regulatory issue, we examine the relationship between risk discourse, epistemology and policy. This study presents a critical, postcolonial analysis of divergent risk discourses elaborated in two contrasting Ontario (Canada) government reports preceding that jurisdiction's regulation of acupuncture, the world's most widely practised TM therapy. The earlier (1996) report, produced when Ontario's regulatory lobby was largely comprised of Chinese medicine practitioners, presents a risk discourse inclusive of biomedical and TM knowledge claims, emphasizing the principle of regulatory 'equity' as well as historical and sociocultural considerations. Reflecting the interests of an increasingly biomedical practitioner lobby, the later (2001) report uses implicit discursive means to exclusively privilege Western scientific perspectives on risk. This report's policy recommendations, we argue, suggest misappropriation of TM knowledge. We advise regulators to consider equitable adaptations to existing policy structures, and to explicitly include TM evidentiary perspectives in their pre-regulatory assessments.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.054 | 0.031 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".