Bibliographic record
Abstract
Book Review| April 01 2016 Review: Sheet Music Consortium Sheet Music Consortium. UCLA Digital Library Program, Project Host; Stephen Davison, Founding Director. URL: http://digital2.library.ucla.edu/sheetmusic/ Judy Tsou Judy Tsou JUDY TSOU is the Head of the Music Library and Affiliate Assistant Professor at the University of Washington. Her recent publications include “Composing Racial Difference in Madama Butterfly: Tonal Language and the Power of Cio-Cio San,” in the edited volume Rethinking Difference in Music Scholarship (Cambridge University Press, 2015), and “Ether Today, Gone Tomorrow: 21st-Century Sound Recording Collection in Crisis,” Notes (forthcoming). Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2016) 69 (1): 255–263. https://doi.org/10.1525/jams.2016.69.1.255 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Judy Tsou; Review: Sheet Music Consortium. Journal of the American Musicological Society 1 April 2016; 69 (1): 255–263. doi: https://doi.org/10.1525/jams.2016.69.1.255 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of the American Musicological Society Search Historically, popular sheet music has been viewed as the poor cousin of regular music scores because it was not considered relevant to serious music scholarship. But there is currently serious work under way in this field, raising many interesting questions in relation to the publishing history, reception, collecting, even artwork and other decorative imagery of the sheet music that once lurked in piano benches, then slipped into attics, and now sits quietly on library shelves. Projects such as the Sheet Music Consortium (SMC) can advance this work considerably, permitting scholars to search in several collections at once with new precision. The project is a collaboration among libraries to build “an open collection of digitized sheet music using the Open Archives Initiative Protocol for Metadata Harvesting,” or OAI-PMH.1 Metadata, technically, is data about data. Practically speaking, the metadata, which amounts to the indexing of names, titles, publishers, and so on,... You do not currently have access to this content.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.057 |
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".