Bibliographic record
Abstract
Network music foregrounds thematerials and processes of communicationand in so doing repositions the acousmatic and other strata of electroacoustic music practice. The type of network music considered in this paper, at base defines a member of its category as music which undergoes an electrical-optical conversion, referring to its transport over fibre-optic research network backbones. A more compelling motivation for us is the realisation that network music entails the exploration of disjunctchronotopicframes (stated less poetically as ‘latency in the network’) using probes of sonic material travelling near the speed of light. This article is an overview of a three-year project investigating music performance over high-speed research networks, a project funded by the Canada Research Chair programme (Syneme). The aim of the project was fourfold: to investigate aspects of physical and social networks in the production of network music (The Network); to investigate a branch of study continuing but critically distinct from Internet music as marked by ingenious strategies mounted to overcome the conditions of slow networks (Liveness); to embed ourselves in new practices (Telemusic Studio) and technologies (Artsmesh); and to compose network music pieces (Net Works). Our narrative picks up from where high-speed P2P networking crosses a threshold producing a successor to the Internet akin to the methodological shift that occurred in electroacoustics when CPUs achieved rendering speeds that allowed for real-time audio.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.533 | 0.176 |
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".