Keeping the Song Alive in Mechanical Music Collections of New York and New Jersey
Bibliographic record
Abstract
Music boxes, musical clocks, nickelodeons and similar objects are commonly referred to as mechanical music or musical automata. New York and New Jersey have rich histories of manufacturing and archiving these objects. Often enclosed in display cases, curatorial attention has not always been paid to the music historically central to these objects. Therefore, this study examined how museums connect the materiality of these objects with their associated music. By synthesizing perspectives from museum studies, music history, and the history of design, five collections of musical automata in New York and New Jersey were examined: The Buffalo History Museum, The Herschell Carrousel Factory Museum, Thomas Edison National Historical Park, The Guinness Collection at the Morris Museum and the Cooper Hewitt Museum. Specifically, this project explored how musical automata produced between 1770 and 1930 have been archived, displayed and interpreted. By interviewing curators and analyzing museum collections, it ultimately appears that the curatorial strategies for mechanical music objects in New York and New Jersey are greatly varied. Additionally, a correlation was found between the proportion of a museum’s collection dedicated to mechanical music and how interactive it is for the public.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".