Sounds of Home: A Survey of Local Music Collection Management Practices in Canadian Libraries
Bibliographic record
Abstract
This paper describes the findings of a national survey of local music collection managers in Canadian libraries in 2018. The survey aims to capture a snapshot of local music collection management practices by identifying areas where collection managers make use of specialized skills and competencies and where practices may be improved. An online questionnaire was sent to local music collection managers in Canadian libraries and consisted of 20 questions that addressed demographics, collection scope, collection development, promotion, access, and preservation.The results show that local music collections are diverse in scope and include a wide range of formats. Many include archival materials or are described or organized using archival principles. Collection managers use a range of strategies to build and develop local music collections, including working with community members and donors to identify, select, and purchase collection materials. Collections are used most frequently by community members and researchers to conduct scholarly or historical research. Outreach and promotion are areas where collection managers are using diverse strategies, including community engagement, event hosting, and online marketing, to build awareness of collections. Physical and digital preservation practices are being implemented by most participants, and online access to collections is often available through additional collection description or digitization.Collection managers may face challenges due to the unique nature of local music collections. Strategies for collection management, collection development, outreach, or promotion may fall outside traditional professional skill sets or competencies. Areas for development include donor relations, community engagement, and archival collection management.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".