Bibliographic record
Abstract
This paper deals with ontological and epistemological questions about how music, listening and public spaces are intertwined. It introduces selected studies on environmental sounds including Soundscape Studies initiated in Vancouver and its relation to the Bauhaus art movement. Other research traditions presented here are based on the concept of how sound sensations, experiences and memories construct a place. They also state that when examining the relationship between urban sounds and spaces, a researcher should pay attention to the fact that no sound event can be isolated from the spatial and temporal conditions out of which the physical signal is propagated. Furthermore, the paper introduces the concept of transphonia, which refers to the mechanical, electro-acoustic and digital storage, moulding, reproduction and transmission of sound, and how this phenomenon constructs shared urban environments. When applied in public soundscapes, the sound-producing equipment, its applications and its musical content become political, and therefore negotiable. Thus, centripetal and centrifugal sounds which have been selected to attract certain consumer segments whilst repelling others are increasingly becoming the subject of scholarly interest because of their highly political nature. Such sounds are not only musical, but encompass other liked and disliked sounds. A case study on commercial radio and urban public spaces is used to shed light on different aspects of transphonia. It shows that radio music in urban public spaces is largely a side-issue for any the radio stations’ music policy, as their music is not selected with these spaces in mind. Nevertheless, it is evident that radio stations are relatively influential in constructing the sonic environments of shared spaces. This construction is in the hands of a few radio professionals, although it should be noted that these individuals’ positions are far from being monolithic, as they are subject to constantly changing economic, legislative and cultural circumstances. This suggests that a space composed of electro-acoustic sounds would be in constant transformation, too, and it would be a target of constant negotiation and struggle regarding its nature, e.g. in relation to the gender, class, age and socioeconomic status of its users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".