Bibliographic record
Abstract
This study centers on an exploration of latency inherent in long-distance networked music performance as well as its application in the performance of "Network Gyre", a work written for bi-located percussion.The research examines time delays in two ways: 1) by creating a series of exercises used to familiarize performers with the nature of network latency and 2) by providing an example of highly rhythmic music written for the network.Notational methods used by composers such as Steve Reich and John Cage are used to address communication and rhythmic challenges in network music.Finally, a description of the extra-musical applications and formal structure of "Network Gyre" is provided.The research was made possible through numerous meetings between researchers at the University of Calgary and Dr. Kenneth Fields and percussionist Feng Piaoyang at the Central Conservatory of Music in Beijing in the fall of 2014.We utilized the research network Cybera to establish a high-speed connection and the program Artsmesh as an interface for connecting audio between sites.The outcome was a performance of "Network Gyre" and a series of exercises that display the characteristics of networked performance.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".