Theoretical Perspectives on Singing Accuracy
Bibliographic record
Abstract
Research Article| February 01 2015 Theoretical Perspectives on Singing Accuracy: An Introduction to the Special Issue on Singing Accuracy (Part 1) Peter Q. Pfordresher, Peter Q. Pfordresher University at Buffalo, State University of New York Peter Q. Pfordresher, Department of Psychology, 362 Park Hall, University at Buffalo, Buffalo, NY 14260. E-mail: pqp@buffalo.edu Search for other works by this author on: This Site PubMed Google Scholar Steven M. Demorest, Steven M. Demorest Northwestern University Search for other works by this author on: This Site PubMed Google Scholar Simone Dalla Bella, Simone Dalla Bella University of Montpellier 1, Montpellier, France Search for other works by this author on: This Site PubMed Google Scholar Sean Hutchins, Sean Hutchins The Royal Conservatory of Music, Toronto, Canada Search for other works by this author on: This Site PubMed Google Scholar Psyche Loui, Psyche Loui Weslyan University Search for other works by this author on: This Site PubMed Google Scholar Joanne Rutkowski, Joanne Rutkowski The Pennsylvania State University Search for other works by this author on: This Site PubMed Google Scholar Graham F. Welch Graham F. Welch University of London, London, United Kingdom Search for other works by this author on: This Site PubMed Google Scholar Peter Q. Pfordresher, Department of Psychology, 362 Park Hall, University at Buffalo, Buffalo, NY 14260. E-mail: pqp@buffalo.edu Music Perception (2015) 32 (3): 227–231. https://doi.org/10.1525/mp.2015.32.3.227 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Peter Q. Pfordresher, Steven M. Demorest, Simone Dalla Bella, Sean Hutchins, Psyche Loui, Joanne Rutkowski, Graham F. Welch; Theoretical Perspectives on Singing Accuracy: An Introduction to the Special Issue on Singing Accuracy (Part 1). Music Perception 1 February 2015; 32 (3): 227–231. doi: https://doi.org/10.1525/mp.2015.32.3.227 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 ContentMusic Perception Search This content is only available via PDF. © 2015 by The Regents of the University of California2015 Article PDF first page preview Close Modal 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".