Bibliographic record
Abstract
Abstract In this article I will discuss the dancer’s physical potential and the sensitive knowledge (‘la connaissance sensible’) that emerges from dance practice. For this, I take Lévi-Strauss’ (2010) theory of the ‘savage mind’ as a reference. This theory is important to understand how the discipline of dance does not need to be justified through modern science (Lévi-Strauss 2010). It is understood that dance operates from sensitive knowledge, while modern science is expressed through the intelligible. I will point out how the dancer operates the sensitive, or dancing, thought, stressing that this type of knowledge is created through interest in how to use it and not through interest in how it serves, or what it means. I will then explain how, through sensitive knowledge, dancers propose new challenges during their aesthetic training in order to develop new body technologies, thereby increasing their expressive potential. For this, dancers need to develop body acuity. In other words, I will discuss the processes that dancers use to transform the perception of the self and of the world through danced gestures. In this process, the dancer is always looking for new challenges; he/she constantly deals with new risks in order to discover new knowledge. I will also discuss the problem that dance faces in traditional academia, which disregards sensitive knowledge and values scientific knowledge.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".