Canada: Assessing the Music Score Collection at the University of Toronto
Bibliographic record
Abstract
The University of Toronto Music Library is the largest music research collection in Canada, with over 350,000 books, music scores, and periodicals, and over 250,000 sound recordings in various formats. Thanks to a healthy acquisitions budget and generous in-kind donations we have exceeded our shelving capacity. Further challenges include open stacks with shelves too close together for users to easily navigate through, poor lighting that makes reading call numbers difficult, and high shelving that the University's Environmental Health and Safety Office determined put materials too close to the sprinkler heads in the ceiling. Changes needed to be made. Tentative plans began for a major renovation of the space in conjunction with the faculty that houses the library; unfortunately, those plans were put on indefinite hold due to geographical constraints, competing University capital project priorities, and (more recently) a new dean of music who needed to focus on accusations of systematic oppression, racism, and coloniality within the faculty's programming and pedagogical practices, in which the library was also implicated. To address these many challenges, the authors of this article began a large-scale project in 2019 to assess space planning and accessibility, update the collections policy, and address issues of equity, diversity, and inclusion within the library's holdings. This article outlines the journey our process took and the key takeaways: a literature review on collection assessment; a summary of our approach to learning about the diversity in our collection; an overview of the collection assessment summit we held in April 2021; designing a user needs survey and interpreting its results, and the next steps in our journey.
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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".