Josquin La Rue Secure Duos Dataset (JLSDD)
Bibliographic record
Abstract
This dataset was created as part of corpus research project in the context of the SIMSSA Project, a SSHRC-Funded Partnership Grant (https://simssa.ca/). Starting with files from the Josquin Research Project, we extracted duos to be able to study two-part counterpoint. The JLSDD (Josquin La Rue Secure Duos Dataset) consists of: 33 secure Josquin duos (Sibelius, Music XML, MIDI, MEI, **kern, and PDF) 44 secure La Rue duos (Sibelius, Music XML, MIDI, MEI, **kern, and PDF) The Sibelius templates used to create the corpus In addition, we have made the following sets available (all discussed in the paper below): Josquin duos (not secure) La Rue duos (not secure) If you use the dataset, please cite the following work: Cumming, Julie E., Cory McKay, Jonathan Stuchbery, and Ichiro Fujinaga. 2018. “Methodologies for Creating Symbolic Corpora of Western Music before 1600.” In Proceedings of the International Society for Music Information Retrieval Conference, 491–98. Paris, France. Paper is available here: http://jmir.sourceforge.net/publications/cumming18methodologies.pdf The files were initially uploaded to GitHub; v1.1 of the repo is equivalent to this dataset and is linked to Zenodo here
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.050 |
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".