Music Librarians on Local Music Collections
Bibliographic record
Abstract
Local and regional music collections are a topic of increasing interest in music librarianship in recent years. With a panel devoted exclusively to this topic at the 2013 Music Library Association conference, papers published in Fontes1 and Notes,2 and the current issue of CAML Review devoted to this theme, it appears that music librarians may be refocusing their efforts on local music. This paper provides an overview of the discourse on local music collecting practices in libraries, and addresses the challenges and opportunities inherent in this endeavor. The scenes approach from popular music studies is presented as one possible body of theory useful for music librarians interested in collecting local music, followed by an account of the author’s experience in developing a local music collection at the University of Alberta Music Library.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.045 | 0.034 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".