Bibliographic record
Abstract
As Michel Serres states, “The one who has power is the one who has the source and emission of sound” (1982). The sudden soundlessness of our COVID-19 existence is one of the largest pandemic challenges facing musicians. While some argue that there should be a hiatus on creation, others are embracing music’s adaptations to less traditional forums and formats. As a public high school teacher and conductor-educator with a youth-focused private organization, I am experiencing first-hand the improvisations, challenges, triumphs—and attendant burn-out—of rapidly adapting new spaces in which to keep my musical communities intact. Fellow conductor-educators near and far, working with populations at all ages and stages, are also bravely forging onward, rejecting sound-less and ensemble-less realities by adapting online. This all begs the questions: What does the near future hold for choral singing? And what will singing ensembles look like on the other side of current restrictions? Drawing together personal experience, informal interviews, explorations of the transformations of public and private space, sound and media studies, drift methodology, and the proliferation of recent articles in news and arts media, this essay investigates the novel spaces being created by and for choral arts educators amidst the uncertainties of what new reality awaits us on the other side of the screen-scape.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".