Bibliographic record
Abstract
Instrumentation: String Quartet and Interactive Electronics\nPerformers of Premiere: Australian String Quartet & Luke Harrald\nOver the past decade there has been a rapid increase in interest in interactive computer music performance, particularly in the area of interactive systems that are able to make active contributions to music performances with live instrumentalists. This area of research has been typified by groups such as the Live Algorithms for Music (LAM) research network (UK), and Musical Metacreation (MUME) research group (Canada). “Distant Front” continues Harrald’s research in interactive computer music performance, exploring technology relevant to this area including pitch tracking and real-time generative composition. Aesthetically, the work responds to that of painter Fred Williams, and aims to depict the flat expanse of the Adelaide Plains. The interactive and generative elements of the work are used to explore the idea that although a landscape may appear the same, over time the details of the landscape are infinitely different. To this end, the string quartet part of the work remains static each time it is played, but the computer generates new electronic parts in response to the string quartet each time the work is played.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.657 | 0.333 |
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".