Bibliographic record
Abstract
What is "research data" for music researchers and performers? How can music librarians develop their knowledge and skills to better meet the research data needs of their constituents, and contribute to the data-intensive turn in academia? This panel will explore the research data movement in libraries and its relevance to music librarians. Panelists will examine the diversity of music research from a data-oriented perspective, and provide examples of these data as drawn from case studies of various music research projects. We will discuss who creates the data, and how it is used, reused, shared, and discovered, as well as the types of music data appropriate for deposit in an institutional repository. Examples of topics covered will include personal archiving, institutional repository guidelines for data, ethical and intellectual property rights considerations, and the role of research data in music digital humanities projects. Attendees will gain an understanding of how music librarians can participate in research data services at their institutions, as well an understanding of the expertise they can contribute to data-related conversations. Panelists include Amy Jackson, Director of Instruction and Outreach at the University of New Mexico, Sean Luyk, Digital Initiatives Projects Librarian at the University of Alberta, Francesca Giannetti, Digital Humanities Librarian at Rutgers University–New Brunswick, Anna E. Kijas, Senior Digital Scholarship Librarian at Boston College, and Jonathan Manton, Music Librarian for Access Services at Yale University. Slides available at http://dx.doi.org/10.17613/q6q4-r940.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.044 |
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".