Bibliographic record
Abstract
This book is an introduction to music sketches. Its goal is to provide the reader with the knowledge and skills necessary to undertake a study of composers’ working documents. The field of sketch studies is changing. Numerous reasons account for this. The impact of electronic technology on the composition, performance, dissemination and conservation of music, the crumbling of the Classical Canon, the weakening of the strong work concept and the concomitant rise of ‘performance studies’ are only a few of the factors that are having a strong impact on music cultures today, as well as on how we study them. Examining how music has been and continues to be composed is no exception. For scholars working in the late nineteenth and early twentieth centuries, a musical sketch was some kind of handwritten document, normally consigned to staff paper. Today a musical sketch can also be data stored on magnetic tape, on a vinyl disc or in a digital device. The book is intended to prepare the reader for this rapidly evolving field. In writing this book, I have endeavoured to strike a balance in my choice of case studies. The reader will find examples of well-known and lesser-known compositions written by the famous and the not-so-famous. Some will be disappointed to discover that the work of composers or scholars they were expecting to find is absent. In the space allotted to me, I have attempted to present a selection that judiciously covers both the time frame (ca. 1600 to the present) and the cultural contexts addressed in this book.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.455 | 0.291 |
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".