Bibliographic record
Abstract
We met in Ottawa—my brother Ronald and I from Los Angeles and my sister Nuria Nono from Venice—to inaugurate the Canadian presentation of a multimedia exhibition that we had produced about our father, in the hope that it would inspire others to learn about his life and works. Professor James Wright invited us to Ottawa to open the exhibition, which he had rescued from the east coast of the United States, and to attend a concert series and symposium devoted to our father's work, hosted by Carleton University. We were all impressed by the schedule of events and felt that through the comprehensive presentation of lectures, concerts, and the exhibition, others would have the opportunity to experience the breadth of our father's life and works. Professor Wright had not only transferred the multi-media exhibition to Canada but had organized an imposing series of events that would encourage the performance and study of Arnold Schoenberg's compositions. Canada has a history of being Schoenbergfriendly (one thinks of the film producer Larry Weinstein, and, of course, Glenn Gould, among others), and our family was delighted to accept this invitation to take part in the events, and to visit Ottawa for the first time. The presentations by participants from Canada, the United States, Austria, and Australia were consistently engaging and excellent. As a non-musicologist, I acknowledge that I prefer listening to music about words rather than words about music . But the combination of lectures and performances resulted in an altogether satisfying and music-enhancing experience. Our family contribution included the opportunity to add a personal touch to the proceedings by discussing our early memories of growing up in Los Angeles. Following the chamber music theme we were able to recall the many visiting performers and ensembles that had performed in our home. But we also revealed the music that we sang together—our ladies club and gentleman's anthems, of course composed by our father, and a special song that he had written for Nuria (the “Nullele-Pullele” lied). And we had the opportunity to discuss and display the many games that he made for us, the tennis scoring system which he developed for my brother in order to help them both analyze the match, and some of his “tinkering” inventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.376 | 0.263 |
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".