Bibliographic record
Abstract
1] I was reminded, in reading the opening chapters of 黎青主 Li Tsing-chu's 音樂通論 in Edwin K. C. Li's elegant English translation, of my experience, in the late spring and early summer of 2018, of visiting Hong Kong for the first time-how that city's built environment, natural setting, mixture of cultures, babel of languages, range of foods, and so on could seem (to someone born and partly raised on the west coast of Canada-not irrelevantly, in this context, another former British colony) at once profoundly familiar and exceedingly strange.Li Tsing-chu's book is, among other things, a call-if not, perhaps, entirely to abandon received Chinese ways of conceptualizing music, then at least to supplement these with ideas borrowed from "the West" (西方), by which he essentially means Germany, where he lived and studied between 1911 and 1922.So for a reader like me, whose grounding in academic musicology was still largely shaped by these same Germanic traditions, a striking feature of the text is how it reflects familiar tropes and themes back through the glass of a very different intellectual sensibility.
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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.075 | 0.033 |
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".