Bibliographic record
Abstract
Every two years, Memorial University is privileged to welcome a gathering of some of the greatest minds and most creative forces in the world of choral and vocal music to a province that is recognized for its historically deep vocal traditions. The Phenomenon of Singing International Symposium was conceived to provide a forum for the sharing of research, knowledge, experience, and practice related to singing, a pan-human phenomenon. We are constantly amazed by what has become a mainstay event in the world of singing and the voice. The Symposium continues to engage scholars, performers, pedagogues, historians, scientists, linguists, sociologists, and psychologists in exciting dialogue, debate, and artistry. Participants present research and initiate dialogue about all aspects of the human act of singing, exploring new ways of singing and creating a new awareness about singing. The Symposium is truly a unique, interdisciplinary sharing of voices that takes place in a carefully crafted, intimate, and nurturing environment. The Faculty of Education is delighted to partner with Festival 500 in offering this experience to the academic community. We are very proud of this event and the academic work that comes out of it. The Faculty of Education is pleased to support this publication of selected papers from the 2005 Symposium. For those not able to attend the Symposium, it is our hope that this publication wiIl provide a glimpse into the rich conversations and sharing that occurred.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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".