Listen to the Theatre! Exploring Florentine Performative Spaces
Bibliographic record
Abstract
A music performance space constitutes the frame as well as the content of the listeners’ experience. The acoustic environment forces continuous negotiations that differ according to a listener’s role and position as conductor, performer or audience member. The aim of this research is to investigate the acoustics of a performative space, the Teatro del Maggio Musicale Fiorentino in Florence, following two complementary paths, both based on an interactive model. The first one offers an impulse-response experience: the user can virtually explore the opera hall by choosing between the binaural reproductions of 13 different listening positions whether off-site (using an audiovisual web app) or on-site, through bone conduction headphones.The second one is about the aural and visual perception of a rehearsal of the romance "Una furtiva lagrima" from Donizetti’s opera L’elisir d’amore. Through the use of ambisonics recordings, 360 degrees videos and a virtual reality headset, the user can experience the performance from three different points of the theatre alternatively: on stage, in the orchestra pit and in the audience area. The three different perspectives can be switched instantaneously using a remote controller.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".