Explorations in Schenkerian Analysis
Bibliographic record
Abstract
Displays the range and diversity of Schenkerian studies today in fifteen essays covering music from Bach through Debussy and Strauss. Explorations in Schenkerian Analysis is a collection of fifteen essays dedicated to the memory of Edward Laufer, an influential advocate of Schenker's method. The chapters are presented in chronological order by composer, opening with Charles Burkhart's contribution, which is presented as a letter to Edward Laufer (written before his death), and ending with excerpts from Stephen Slottow's 2003 interview with Laufer (in an appendix). Whilethe unifying focus is Schenkerian analysis, there is considerable variety in the approaches taken by the contributors. There is also variety in the composers represented, ranging from Bach to Debussy and Strauss. The volume thusdisplays the scope and diversity of Schenkerian studies today. CONTRIBUTORS: Mark Anson-Cartwright, David Beach, Matthew Brown, Charles Burkhart, L. Poundie Burstein, Timothy L. Jackson, Roger Kamien, Leslie Kinton, SuYin Mak, Ryan McClelland, Don McLean, Boyd Pomeroy, William Rothstein, Frank Samarotto, Stephen Slottow, Lauri Suurpää David Beach is professor emeritus and former dean of the Faculty of Music, University of Toronto. SuYin Mak is associate professor of music at the Chinese University of Hong Kong.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".