Freedom in the Invitation: Lilith & C<sup>ie</sup>’s <i>Invisible</i>
Bibliographic record
Abstract
Lilith & Cie’s Invisible is a participatory durational dance piece that aims to “make the intelligence of the collective visible.” Invisible spans three consecutive days and features ten improvising dancers and a charming black dog. In May 2022 at Montreal’s OFFTA, the piece featured dancers Ariane Boulet, Rachel Harris, Emmanuel Jouthe, Abe Simon Mijnheer, Caroline Namts, Charlie Prince, Luce Lainé, Charles Brécard, Zoë Vos, and Silvia Sanchez. The following is an excerpt of a conversation between Invisible’s dramaturg Kathy Casey and choreographer Aurélie Pedron on how they structure the invitation to indirectly influence audience behaviour, balancing freedom with limits. Invisible is almost entirely guided by audience input, inviting participants to feed into the collaborative action and catalyze dancers through rearranging furniture and plants, DJing through our phones, changing the lighting, leafing through show records, or simply listening and witnessing in silence. In order to gently gesture toward both the expansiveness and limitations of this audience participation, Invisible begins when participants enter the show lobby and are presented with a board game and a deck of cards that offer a map and guide to the 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".