Bibliographic record
Abstract
This article employs Julia Kristeva’s concept of the chora as a means to describe and analyze the dancing body and the body of the spectator. In Revolution in Poetic Language, Kristeva posits the chora as analogous to vocal and kinetic rhythms of the body. As an audience member who also trained as a dancer, I find my body responds instantaneously and rhythmically to dance performances, thereby connecting to the chora. Subsequently, the act of writing becomes a physical manifestation of the theatrical experience. My research questions include: what role does the body play in the transmission of dance to language? How is the essence of the chora transferred from dancer to spectator in the experience of watching a performance? The writings of Julia Kristeva, Roland Barthes and John Martin provide important theories of the body that aid in answering these research questions. With an awareness of the chora in each subject, I examine the transfer of the chora from the dancer to the spectator in the work of Montréal choreographer Marie Chouinard, specifically the male solo Des feux dans la nuit. Marie Chouinard stands out as one of Canada’s most successful and internationally recognized contemporary choreographers. This article considers the impact of Chouinard’s dancing body on the spectator.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".