Bibliographic record
Abstract
Webcast sponsored by the Irving K. Barber Learning Centre and hosted by the Richmond Public Library (RPL) as part of its The Joy Of Reading lecture series. Eileen Chang is widely recognized as one of the most important Chinese writers of the twentieth century, but few people know about her many English language writings. This talk introduces this side of her fascinating career by presenting some recent research on her translation work. Presented by Prof. Chris Lee, Dept. of English, UBC. Speaker Bio Chris grew up around Vancouver and went to high school on the North Shore. He graduated from the Honours English Program at UBC and also student critical theory and Asian American Studies at the University of California, Irvine. From the West Coast of Canada he moved to the East Coast of the United States to attend graduate school at Brown University. Before writing his dissertation, he spent a year in Beijing taking classes and doing research. He returned to Vancouver in 2006 and took up an appointment as Assistant Professor of English at UBC in 2007. Chris has been a Faculty Fellow of the College since 2007. Chris’ areas of research include Asian North American literatures and cultures, American Studies (with a focus on race/ethnicity and transnationalism), critical and literary theory (especially the Frankfurt School) and aesthetic philosophy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.221 | 0.079 |
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".