Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.This article presents a corpus of Cantonese popular music (Cantopop) and discusses patterns of tone-melody correspondence in the corpus data. The corpus consists of melodic and textual information on 105 Cantopop songs from 2000–2020 encoded in the Humdrum format. To ensure broad representativeness of the corpus, I selected the songs based on their popularity, as represented by the number of music awards they received from media channels in Hong Kong. The resulting corpus is significantly larger than those examined in previous studies on Cantopop and is selected from more clearly defined criteria. This article discusses two aspects through which Cantopop melodies correlate with Cantonese tones in the lyrics: directionality and interval size. The corpus data provide significant statistical evidence for the strong text-setting constraint regarding directionality. Patterns of interval sizes observed from the corpus indicate a more flexible mapping between Cantonese tones and interval sizes in Cantopop melodies than reported in previous studies. By offering substantial data that are publicly available, this corpus advances the quantitative study of Cantopop and facilitates further research on the genre.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.923 | 0.864 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".