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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".