Bibliographic record
Abstract
There are few art forms as comprehensive and encompassing as opera. Since its conception, opera has been used to express the deepest emotions of the human experience and it has been adapted by designers in a variety of methods and degrees to speak to each generation. In this project, I have proposed the re-imagining of public spaces as opera performance spaces and studied the resulting duality of sound and experience. To explore this topic, I looked to the public spaces on Queen’s University campus for inspiration and found it where Union and University meet. The familiar “scramble” is a central location that students cross on their way to classes many times throughout the day. It is a public space, regimented by the design of traffic lights which prioritize pedestrian crossing on a dependable and predictable circuit. I propose that this location provides an opportunity for the twenty-first century opera designer to use its existing cycle to create a dynamic and unique performance space for opera. While the idea of performing opera outside of the opera house is not new, few have designed opera in a simultaneously public and dynamic space as I am proposing. This fresh approach to opera for the twenty-first century creates an experience for passer-bys and performers that is twofold: the scramble remains a public and utilitarian space meant for getting from one place to another, but it also becomes a performance space in which spectators and performers alike “play their parts”. In short, the resulting experience of sound is hearing the city and the opera simultaneously, transforming the perception of the scramble as a public space into a heightened and transcended 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".