Bibliographic record
Abstract
I do theatre. My artistic work is divided in three main fields: as an actor-clown, as a director and as a professor at the National San Martin’s University’s in Buenos Aires. I work on the link between personal and collective memory. After the creation in 2017 of Eighty Bullets in the Wing (a personal creation in the Argentinian ESMA, the biggest extermination camp in the Americas used by the Argentinian Junta between 1976 and 1983, formerly the Navy’s Mechanic School), in 2019, I was invited by French cultural institutions to Paris, where I established and created the Intimate Model’s Lab . This Lab has been a meeting point for artists, people having experienced traumatic events, therapists, and academics from more than 20 nationalities. Throughout the Labs, theatre companies, museums, universities and cultural spaces from Argentina, Mexico, France, Belgium, Switzerland, the United States and Canada have mixed in interdisciplinary groups to explore this method. In a full immersion, five-day experience the participants work resiliently in group and on their own through different aesthetics tools: Physical play through movement and dance. Objects and image theatre, writing and drawing through personal and collective memory. A plastic work to create the models, a transposed representation of a lived experience.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".