A Listener's Guide to Noise in the Anthropocenic City
Bibliographic record
Abstract
Noise often brings to mind a loud or disturbing sound. When defined as “unwanted sound”, we find that noise is not inherently negative but speaks to a negative reaction to something in one’s environment. Noise is most audible at the scale of the Anthropocenic city; an urban world involving ubiquitous interconnections between human and natural forces. Since the Romantic era, aesthetics has ideologically separated humans from nature, framing the human as a “nuisance” and nature as “ideal”. Henceforth, urban soundscapes have been seen as nuisances deserving of noise control, including noise by-laws, architectural acoustics, and personal headphones. By using sonic methods of active listening, field recording and sound art, this project will take the listener on a virtual soundwalk through space and time along Vancouver’s Seawall. The soundwalk will focus on noise that signifies aesthetic relationships humans have with built and natural environments. As life in the Anthropocenic city challenges us to reimagine our connection to the natural world, this project will amplify noise to demonstrate how aesthetic relationships with environments are revealed through active listening.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.029 |
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".