Bibliographic record
Abstract
RIPM ( Le Répertoire International de la Presse Musicale ) is widely regarded as the most comprehensive resource offering electronic access to music periodicals from the early Romantic era to the twentieth century. Founded in 1980 by H. Robert Cohen, RIPM is the youngest of the so-called ‘4 R's of International Music Research’; its partner initiatives include RISM ( Répertoire International des Sources Musicales ), RILM ( Répertoire International de Littérature Musicale ), and RIdIM ( Répertoire International d'Iconographie Musicale ). Cohen's ambitious project was notably visionary in its use of technology: not only did it use computing from the start (beginning with DOS-based indexing systems), but it was also the first of the 4 R's to explore full text searching. RIPM seeks to address ‘two main problems that have prevented these [historic music] journals from being systematically collected and examined: (1) the limited number of libraries possessing the journals, and (2) the difficulty encountered when one attempts to locate specific information within an available source’. The project has thus focused on collection building, curation, indexing and accessibility. This international cooperative's accomplishments are impressive: as of July 2020, the database contains 527 music periodicals, 430 available in full text complete runs, totalling 996,000 annotated records and 1.47 million full-text pages of music periodicals.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.050 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.059 |
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".