Conducting Pantless: Exploring Internet Communication Technologies to Rehearse Choirs at a Distance
Bibliographic record
Abstract
Near North Voices is a university-community choir in North Bay, Ontario. Founded in 2007, this ensemble has provided an “advanced” choral singing opportunity on the shared Nipissing University/Canadore College campus that has grown to serve the broader community. Rehearsals commence the first week of university classes in September, and follow the academic year with concerts in February and early April. Going into our sixth year of operation, Near North Voices was faced with an operational crisis – the injury and incapacitation of our Artistic Director. As questions of sustainability emerged, the Artistic Director and Advisory Board struggled to plan a path forward. Should the choir be suspended pending the Artistic Director’s recovery? Would the choir survive as a cultural entity, or would a suspension wreak havoc on recruiting and retention? Given the ensemble’s remote location in a community with few professional musical leaders, as well as the absence of a budget for such a hiring, the possibility of recruiting stand-in conductor was an unlikely. When the choir’s Assistant Conductor-in-Training valiantly stepped forward, questions of recruitment and retention emerged as they relate to the cult of personality within ensembles. Would choir members follow an amateur conductor who, while a capable musician, had minimal training in choir pedagogy and conducting? Meanwhile, assigned to home pending surgery, the Artistic Director’s personal/professional need for continued professional practice as well as for connection with the community was challenged by requirements of the insurance company and university to live as a detached and non-operational “disabled” person. This paper follows the ensemble’s journey as we used distance technologies to achieve our musical and pedagogical goals the while maintaining a semblance of normalcy by allowing the Artistic Director to “conduct” from home. Later development of distance technologies also supported chorister engagement from remote locations. The challenges and benefits of using distance technologies in real-time and recorded ensemble rehearsal situations to facilitate conductor and chorister mobility are discussed from logistical, pedagogical and psychological perspectives. Several distance communication technologies are critically reviewed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".