Transforming Body, Emerging Utterance: Technique Acquisition at a Puppet Theater
Bibliographic record
Abstract
This paper describes the moment when a new body technique is acquired, using a case study in which three puppeteers manipulate a single puppet together. Although phenomenology assumes that the world is always “already there” before reflection begins, we can still ask how a sequence of movements is acquired. Struggling to learn puppet choreography in a training session, the learner’s body encounters difficulties because it cannot easily imitate the proper movements. At the same time, the puppet master cannot easily explain those movements because he or she is so familiar with them. The communication between instructor and learner requires a kind of reflection that helps the learner transform and attain competency; this reflection is different from a dualistic disembodied form of thinking thatuses abstract representation. The focus is on the precise coordination of gestures and onomatopoeic utterances that emerge through improvisation in the learner’s trial movements. It is not just “a process of thinking,” but an experience that evokes “a synchronizing change of my own existence, a transformation of my being” (Merleau-Ponty, 1962, p. 213), through which the puppeteer facilitates his or her own body’s comprehension of new movements. Using puppetry as an example not only illuminates the phenomenon of learning a bodily skill, but also reveals the dynamics of our bodies, which can enliven our conversation, engender our transformation, and realize our being-in-the-world.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".