Learning from Game Live Streaming: A Model for Networked Music Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We propose a model of networked music performance to help musicians think through and evaluate their online music productions. First, we utilize a model of game live streaming by T. L. Taylor (2018) to analyze several of our own productions of distanced networked music performances of ludified notation (Peles 2023). Building on Taylor’s model and additional scholarship on music performance, both networked and not, we then extrapolate the insights gained to networked music performance in general and derive a list of key considerations for the analysis, critique, and ideation of networked music performances. Finally, we demonstrate the use of the model through (1) the analysis of a networked music project by other artists, (2) the critique of one of our own recent productions, and (3) the ideation of a potential future performance. Online music production has become an important mode of musicking and continues to grow as a practice. We offer this model as a tool for both practice-based and scholarly work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it