Bibliographic record
Abstract
Music Information Retrieval (MIR), and ISMIR annual conferences offer a rich panoply of intellectual and cultural diversity. We map the evolution of MIR using conference papers from 2000 through 2005. Results indicate tight thematic coherence in the domain around the problems of information retrieval and classification, and the locus of most research within computer science departments.Les conférences annuelles sur le repérage d'information musicale (MIR) et ISMIR offrent une riche panoplie de diversité culturelle et intellectuelle. Nous traçons le portrait de l'évolution du repérage d'information musicale en utilisant les communications des conférences de 2000 par 2005. Les résultats indiquent une correspondance thématique étroite dans le domaine touchant les problèmes de repérage et de classification d'information et dans la position de la plupart des recherches des départements d'informatique.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.065 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".