Collection Development in Canadian Academic Libraries
Bibliographic record
Abstract
Music librarians with shrinking acquisitions budgets, crowded shelves, and pressure to create more student study space face fundamental questions: How do we sustain the quality of the music collection with limited funds? How can we be proactive with collection development when so much is beyond our control? Houman and Carolyn will present survey results that capture a snapshot of the current state of music acquisition funds and collection building activities in Canadian academic libraries. In particular, they will cover how these funds are organized, where they are being spent, and how fluctuations in institutional support for library collections may impact music collection-building mandates across Canada. Since the fall in the Canadian dollar and the lower purchasing power of the library, this survey may be used to develop contingency measures to examine potential changes in the area of music collection development. Jan and Kevin will review the pros and cons of two potential responses to shrinking budgets. First, seeking donations (monetary or in-kind). Endowed funds can increase acquisitions budgets, but are vulnerable to market fluctuations. They can also come with donor restrictions. In-kind donations add value to our collections but require staff resources to process and catalogue. Second, collaborating with other music librarians to highlight unique collections and avoid duplication of effort. Successful collaboration depends on like personalities, geography, and institutional support. Do music librarians in Canada have enough purchasing power to negotiate with vendors? Can librarians serving different institutions and patron communities find a coordinated future together?
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".