Bibliographic record
Abstract
In contrast to those websites already discussed by previous authors in this ongoing series of reviews—which have mostly concentrated on the (naturally, first-rate) products of established and institution-based research projects—the focus here is on online resources that are more akin to repositories of public-domain, copyright-free material. The defining feature of the majority of these is that their content is user-generated (whether by a single creator or by a network of collaborative contributors) and as such does not carry any sort of institutional imprimatur; furthermore, in almost all cases users incur no charge to access the resources. Needless to say, such a model has both advantages and disadvantages for those on the hunt for early music material, but—as has been noted previously—the informed use of several of these resources in conjunction with one another, in order to mitigate the limitations of each individual website, can prove rewarding and hopefully successful. A natural starting point for any discussion of this kind of online resource is the International Music Score Library Project (www.imslp.org)—also known, for obvious reasons, as the Petrucci Music Library, but now universally referred to by its initials IMSLP—perhaps the leading online resource for free musical scores. Launched in 2006 by Canadian Edward W. Guo during his second year studying composition at the New England Conservatory, IMSLP is now far and away the largest website of its kind—it hosts nearly 500,000 scores of over 150,000 works, all of which are public-domain or licensed material—and has become such a mainstay of digital music research thanks to its scope and ease of use that it has been recommended as a resource for students at universities all over the world. Additionally, IMSLP hosts nearly 60,000 public-domain recordings of works for which the score is also available; these tend to be either out of copyright, historic recordings or user-created versions generally shared under a Creative Commons licence. (Since 2015 the site has also offered subscriptions, the benefits of which include advertising-free browsing, the removal of an enforced 15-second wait before the downloading of certain files and access to the Naxos Music Library of digital recordings.)
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.699 | 0.706 |
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".