Musical Excellence: Strategies and Techniques to Enhance Performance
Bibliographic record
Abstract
Book Review| September 01 2005 Musical Excellence: Strategies and Techniques to Enhance Performance Musical Excellence: Strategies and Techniques to Enhance Performance Edited by AARON WILLIAMON Oxford University Press, Oxford: 2004 CHRISTINE BECKETT CHRISTINE BECKETT Music Department, Concordia University, Canada Address correspondence to: Dr. Christine Beckett, Concordia University Faculty of Fine Arts, Music Department, RF building, 7141 Sherbrooke St. W., Montreal QC CANADA, H4B 1R6. E-MAIL cbeckett@vax2.concordia.ca Search for other works by this author on: This Site PubMed Google Scholar Address correspondence to: Dr. Christine Beckett, Concordia University Faculty of Fine Arts, Music Department, RF building, 7141 Sherbrooke St. W., Montreal QC CANADA, H4B 1R6. E-MAIL cbeckett@vax2.concordia.ca Music Perception (2005) 23 (1): 101–102. https://doi.org/10.1525/mp.2005.23.1.101 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation CHRISTINE BECKETT; Musical Excellence: Strategies and Techniques to Enhance Performance. Music Perception 1 September 2005; 23 (1): 101–102. doi: https://doi.org/10.1525/mp.2005.23.1.101 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. © 2005 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA. ALL RIGHTS RESERVED.2005 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".