Bibliographic record
Abstract
Local music holds tangible meaning in the form of evidence of historical events, development of musical practices, documentation technologies, cultural artefacts, It also holds intangible meaning in the form of associations with memory, nostalgia, and feeling. Studying collections of local music and collecting practices (as opposed to individual artifacts, musical genres, or music scenes) provides us with unique understandings of the interplay between local, regional, and national music histories. This paper will discuss the findings of a survey of local music collecting and collections in Canada, which will be conducted in early 2018. We will provide an analysis of the collected data, which investigates behaviours, preferences, and beliefs about local music collections and collecting in libraries. Specifically, the themes of investigation include collection management, collection development, access, digital tools, promotion, challenges, and future planning related to local music collections. This research seeks to understand the state of local music collections and collecting in libraries across Canada. Specifically to: 1) to identify where collections of local music are held, what music(s) they document, and what evidentiary value they possess; 2) to understand the perceived value of collecting local music, and 3) to record local music collection management practices currently in use and where these practices may be improved.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.029 | 0.003 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.006 |
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".