Dance Day 2022 in conversation with Marc Vanrunxt, Liz Kinoshita & Zoé Lakhnati
Bibliographic record
Abstract
It’s 2022. Contemporary dance started to color the Flemish cultural landscape about 40 years ago. It’s about time to evaluate the state of affairs. For this occasion, Dag van de Dans asked me to engage in conversation with three dance artists, each in very different career stages. Belgian choreographer Marc Vanrunxt was there when it all started. He has made more than forty creations since the early 1980s, a period during which he has played an important role in the development of the Flemish Wave. Today he shares his knowledge as a teacher at KASK in Ghent and as artistic advisor for a significant number of choreographers. His company Kunst/Werk is located in Antwerp. Liz Kinoshita moved from Canada to Belgium. She studied at P.A.R.T.S. in Brussels from 2004 to 2008. Since then, she has worked with ZOO/Thomas Hauert, Tino Sehgal, and Hiatus/Daniel Linehan among others. Alongside she makes her own work, in which she examines the mechanisms of the musical. Zoé Lakhnati is about to graduate from P.A.R.T.S. this year. She was born in the South of France. She’s trained as a professional ballet dancer, but wanted to explore her own artistic voice more in Brussels. We can follow Zoé’s journey at P.A.R.T.S. on the podcast Generation XIII created by Delphine Hesters. I wonder if they all know each other.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.262 | 0.134 |
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".