Bibliographic record
Abstract
Earlier this year, I began an online survey of choral singers to find out how singers respond to conductors and what inspires these singers to deliver their best performance.My intention is to share constructive feedback with conductors, facilitating a deeper understanding of what singers value in their leaders.This is an ongoing project and I hope the survey will continue to grow.Please consider this presentation an interim report, as I hope to have many more responses.Some conductors bristle at the idea of such a survey, perhaps because feedback is not traditionally used in the choir-conductor relationship, though it has proven to be a useful tool in other disciplines.In academic institutions we ask students to evaluate a course so that it can be improved.In business, feedback is used to increase productivity.But, in some situations, asking for group opinion can open the floodgates of total chaos.A ship's captain, making a strategic decision whether to unfurl a jib or lower a mainsail, rarely turns to the crew for a show of hands.A good leader, whether mariner or musician, must know the charts and make difficult decisions in troubled water or in calm sea.One respondent describes it like this:
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".