Bibliographic record
Abstract
Marie Brassard’s innovative use of sound technologies to alter her onstage voice raises the stakes for considerations of theatrical mediality in our present situation. Working with sound artist Alexander MacSween in her most recent productions, including her ongoing Peepshow, Brassard has employed digital sound and altered voice “as a natural extension of the body” (“Peepshow”), transforming herself into the site of multiple voices, multiple identities. In the early the twentieth century, new optic technologies took centre stage in conceptual innovations with theatrical practice. Movements as different as futurism, Dadaism and Bauhaus each emphasized the importance of visuality and visual space over that of sonority and acoustic space, largely drowning out human speech along the way. Until well after World War II, innovations in acoustic technologies nearly always took the backseat in onstage performance. As Christopher Baugh has noted, early twentieth-century developments in lighting techniques and filmic projections were perceived by audience members as “real,” as part of the experience of the “live” – after all, light perceived was light itself – but to the ear of a public not yet accustomed to new techniques of listening, early incarnations of sound reproduction seemed mere “imitations” and distinctly artificial (203).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".