Bibliographic record
Abstract
This article discusses my pedagogical approach towards teaching dance, movement, and theatre to artists with cognitive disabilities. I have developed a training method for artists participating in my classes and productions that invites individual creative exploration and professional rigour. My inclusive dance-theatre company, Les Productions des pieds des mains, creates productions that are presented on professional artistic platforms and not limited to the context of disabled art presentations. Our productions are regularly subject to the same selection criteria as would be a non-inclusive company. With this in mind, I aim for excellence. I work towards creating high quality shows while helping artists with a disability push past their perceived limits and surpass themselves. In this article I will share my findings and strategies, perfected throughout my many years of experimentation, explaining how to reach a high-level of quality in inclusive productions, as well as how I help artists with a disability reach a level of quality in their performances which allows them to find gainful employment within the artistic domain.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".