Music as Knowledge in Shamanism and Other Healing Traditions of Siberia
Bibliographic record
Abstract
Several presenters made the point that one cannot look at narrative alone, without taking into account the music, dance, and drumming that, in many settings, go along with it. One of these presenters was Marilyn Walker, who has had the good fortune to work with healers in Siberia. Although academic in approach, Marilyn’s paper also recognizes the importance of experiential ways of knowing. In her Quebec City presentation, she shared some of this experiential dimension by showing and commenting on videotaped segments featuring three Siberian healers. Walker’s paper discusses healing at several levels. In addition to several healing dimensions that she lists at the end of her paper, she mentions the physiological effects of music, dance, and drumming. Current research is leading to a better understanding of how trauma affects the brain and the body, and ways that various therapies, including new therapies focusing on sensorimotor effects, can promote healing. Along with these developments has come a greater appreciation and understanding among some mental health practitioners of some of the neuropsychological processes by which traditional practices such as narrative, singing, drumming, and dancing, may bring about healing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".