Critical Review of Healing Elements: Efficacy and the Social Ecologies of Tibetan Medicine
Bibliographic record
Abstract
This article offers a critical review of Healing Elements: Efficacy and the Social Ecologies of Tibetan Medicine. The ethnography provides rich and comprehensive insights regarding the triumphs and tribulations of Sowa Rigpa (traditional Tibetan medicine) as the medical system is translated across diverse contexts to ensure its continuity within the globalized world; however, these insights can be broadened by more deliberately acknowledging and investigating the (post)colonial subtexts underlying these translations. Incommensurability emerges throughout the ethnography in the form of tensions that arise as tacit knowledge is translated to explicit knowledge in the quest for legitimization. It is argued that expounding the nature of this incommensurability by engaging with rather than rejecting polarized notions of “traditional” and “modern” paradigms can reveal that non-biomedical medical systems and medically pluralistic contexts more broadly are inundated by (post)colonial processes. Borrowing Blaser’s (2013) notion of “Sameing,” it is demonstrated that translation involves (post)colonial processes of assimilation, as Sowa Rigpa is rendered visible through Good Manufacturing Practices (GMP), and appropriation, as it is made palatable through pharmaceutical commodification. Furthermore, it is argued that these processes mobilize mimesis and essentialization to transform Sowa Rigpa into a system that is both legitimized and acquiescent to the imperatives of varying external regimes. The simultaneity of these effects and the position that they are not mutually exclusive is asserted throughout the review as further evidence of (post)colonization.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".