Grasping Heaven and Earth (Qian Kun Zai Wo): The Body-as-Technology in Classical Chinese Medicine
Bibliographic record
Abstract
Shifting focus from the patient’s body to the healer’s body, this essay focuses on how Chinese physicians instrumentalized their bodies to heal (ie, body-as-technology) and their hands to think with (ie, hand-memory techniques or simply, hand mnemonics). When physicians used their hands to memorize concepts related to clinical practice, calculate with time variables, and carry out ritual gestures intended to reduce risk, improve fortune, and even cure, their hands became extensions of their minds. This essay has three parts that follow the discovery process of the author’s research on hand-memory techniques found in Chinese medical texts. The first part “Divination and Revelation” explains the significance of how the author first learned about Chinese divination practices that used hand mnemonics. The second part “Original Frame” introduces the scholarship on arts of memory in Europe that informed interpretations of the earliest hand mnemonics found in Chinese medical texts. The third part “Expanded Frame” deploys some concepts from cognitive science to help situate Chinese medical hand mnemonics more broadly as an example of extended cognition. The essay concludes with an important distinction: sometimes Chinese healers’ hands were used separately from their bodies to think through things and sometimes hand and body had to be integrated in order for the healer’s body-as-technology to act as a therapeutically effective instrument.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".