Bibliographic record
Abstract
Martin Daigle skillfully unearths the melodic qualities of the drumset on DRUM MACHINES from Ravello Records, an album featuring the works of composers Pierre Alexandre Tremblay and Sylvain Pohu. Daigle, the 2022 winner of Music NB’s “Innovator of the Year” award, performs two solo pieces that feature innovative applications of technology in this release, producing unique timbres through electronic augmentations of the drumset. These unique flavors paired with Daigle’s adept musicality and inventive grooves make for a one-of-a-kind listening experience that’s sure to stir the senses. The composition La Rage, by Pierre Alexandre Tremblay which features on the album has many various interactions between acoustic and computer-generated musical events. With a series of performance gestures, through written beats, and improvisations, the performer is in constant conflict with the machine that wishes to take over the sound space. While struggling to confront the machine, the performer must face varying power dynamics in the hopes to remain relevant and audible. The composition was one of his most ambitious projects featuring an octophonic speaker setup surrounding the audience. In 2004, this piece illustrated that microphones may serve many different purposes other than their principal use of sound amplification
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.302 | 0.196 |
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".