“She’s a Dear Old Lady”: English Canadian Popular Songs from World War I
Bibliographic record
Abstract
Research Article| December 01 2016 “She’s a Dear Old Lady”: English Canadian Popular Songs from World War I Gayle Magee Gayle Magee Gayle Magee (associate professor, University of Illinois, Urbana-Champaign) is the author of three published books, the most recent of which is Robert Altman’s Soundtracks (Oxford University Press, 2014, in the series Music/Media). Currently, she is completing a book on composer William Bolcom for the University of Illinois Press’s American Composers series. Other recent and forthcoming publications include an edited collection on music in the First World War and two book chapters on music in the British heritage genre. Magee serves as coeditor in chief and project director for the NEH-funded publication series Music in the United States of America (MUSA) and previously served as president of the Charles Ives Society (2010–16). Search for other works by this author on: This Site Google American Music (2016) 34 (4): 474–506. https://doi.org/10.5406/americanmusic.34.4.0474 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Permissions Cite Icon Cite Search Site Citation Gayle Magee; “She’s a Dear Old Lady”: English Canadian Popular Songs from World War I. American Music 1 December 2016; 34 (4): 474–506. doi: https://doi.org/10.5406/americanmusic.34.4.0474 Download citation file: 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 Scholarly Publishing CollectiveUniversity of Illinois PressAmerican Music Search Advanced Search The text of this article is only available as a PDF. Copyright 2017 by the Board of Trustees of the University of Illinois2017 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".