Bibliographic record
Abstract
Presented as part of The Dada Sound project (February 2016) - see more at http://www.citiesandmemory.com/dadasoundsThis ‘study’ began with a one-minute excerpt of a field recording made at a downtown Vancouver street corner on January 30, 2016. The original audio document was made with a zoom H6 with the microphones in an x/y configuration; the audio was then manipulated using the TIAALS software. The TIAALS software was originally developed by researchers at the University of Huddersfield and Durham University to analyze electro-acoustic compositions. It allows the user to select areas of the recorded soundscape––represented on a spectrogram as a combination of time, frequency and amplitude––that may be either filtered or passed over. These areas may also be categorized and played back individually, affording some very interesting possibilities for sonic montage, collage etc. A visual analogy of the filter study presented here might involve taking an image of the street corner and cutting out certain frequencies of light (color, intensity etc.) so that the remaining information is 'abstracted' from the original and becomes something 'new', while still retaining key features of the original document. And indeed, one can still discern the sounds of the buses, voices, street crossing signals and the busker playing the erhu on the left of the audio image (as well as the small patch of distortion caused by a sudden gust of wind). However, thanks to the filtering process, these everyday sounds may now be engaged with in a radically new way. In this way an audio ‘filter study’ resonates with Duchamp’s notion of a ‘ready made’. Nothing has been ‘added’ to the resulting audio. However, like Duchamp’s urinal, bicycle wheel or bottle rack, sonic objects found in mundane experience are detached (filtered) from their normal contexts (e.g. urinal/men’s restroom) and manipulated in various ways in order to aestheticize them."
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.225 | 0.062 |
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".