Bibliographic record
Abstract
P&t&I LoVIlckTwo YEARS AGO, Kate Stevens gave me a call from her home in Victoria, British Columbia.It would plunge me into a fascinating, sometimes irritating, but ultimately rewarding project.Professor Stevens taught in the Department of East Asian Studies at the University of Toronto until her retirement in the late 19808.She taught Chinese performing arts as literature for over twenty years and has made an extensive study of Peking drum singing, a storytelling genre which she performs.I had been and, in the Chinese way of looking at things, still was her student.After she left the university, I continued as a teacher of Performing Arts in China in her place, a post for which she recommended me.Now, she had something else on her mind for me.Would I be interested in writing a book on Chinese opera?A book? Surely, there were quite a number of these already.But what Kate Stevens had in mind was something quite different.That's when she told me about Siu Wang-Ngai and his collection of photographs.Siu Wang-Ngai is one of the most prominent photographers in Hong Kong.A lawyer by profession, he has won international recognition for his photographs.The Royal Photographic Society of Great Britain awarded him a fellowship for his theatrical photography in 1985, and honoured him with a second fellowship in 1989 for his pictorial photography.Mr. Siu's photographs are frequently exhibited and published.He was chair of the Federation of Hong Kong-Macao Photographic Associations and often serves as a judge in Hong Kong photography exhibitions.Kate Stevens explained that one of Siu's ongoing special projects was photographing Chinese regional opera.There are over 300 different regional opera styles in China.In recent years, outstanding troupes with China's foremost performers have visited Hong Kong.Siu began photographing their performances in 1981 and has so far taken over 30,000 photographs of more than 200 operas in twenty-two regional opera styles.These photographs were what would make this book special.Siu was prepared to select a number of shots from his collection personally, but they needed text around them to explain the stories and give background.I was intrigued.In addition to my teaching work, I performed the painted-face role in the Chinese Opera Group of Toronto.I knew what it felt like, if only as an amateur, to wear the bold make-up, carry the heavy costume, and tell a story using conventions from a highly polished theatrical tradition.Maybe I could bring that experience to the photographs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.411 | 0.237 |
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".