Bibliographic record
Abstract
On June 20, 2009, late afternoon, I improvised with Joe Sorbara as part of Lex Non Scripta, Ars Non Scripta: Law, Justice & Improvisation, a conference organised by the McGill Centre for Intellectual Property Policy as well as the Improvisation, Community and Social Practice research project. The performance entitled “Improvised Contemporary Movement and Sound Performance” took place at Sala Rossa in Montreal. I was moving, using corporeal mime and other movement techniques, and Joe was making sound with various percussion objects. Joe and I were improvising based on a score that we developed together. The score was spatially defined in the shape of a line that we followed as we improvised. The coordination of our movements and sounds was also defined. Although primarily Joe was the musician and I was the mover, sometimes the line was blurred between who was moving and who was producing sound. Our improvised performance lasted around 17 minutes. This commentary aims to build on those 17 minutes by exploring the improvisational structure of the performance and examining this structure as the “law” of our improvisation. This text will also reflect on the broader relationship between law and improvisation.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.049 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".