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Record W6992656626

Mapping the underground soundscape : fieldwork among the subway musicians of Toronto

2012· other· en· W6992656626 on OpenAlexaboutno aff

Bibliographic record

VenueOpenGrey (Institut de l'Information Scientifique et Technique) · 2012
Typeother
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSoundscapeMusicalEthnographySound (geography)NegotiationSpace (punctuation)Built environmentUrban environment
DOInot available

Abstract

fetched live from OpenAlex

The term soundscape was developed by R. Murray Schafer to describe "the sonic environment" (Schafer, 1977, p. 274), which encompasses all the sounds that surround us; his studies of urban soundscapes are particularly noteworthy. One soundscape that has been rarely studied is one that is found in many urban centres - the subway. "Mapping the Underground Soundscape" is six-year musical ethnography exploring the Toronto Transit Commission's (TTC) Subway Musicians' Programme and how it provides a unique auditory environment in its stations, passageways, platforms, and thoroughfares. More broadly, this thesis examines the relationship between music and the urban environment. It considers the influence of music on an urban environment (the subway) and how that environment is imagined and represented, and then how that urban environment influences music-making practices. Just like landscapes, soundscapes have a figure-ground relationship. At what point does the music become the figure? If one listens closely, one can hear sounds emanating from a guitar, an erhu, a violin, or keyboards, sounds, perhaps on a first hearing, uncharacteristic of the subway. Through the continual shift of figure and ground of these subway musical performances, the transit system becomes a temporary performance space for those willing to listen. This figure-ground test prompts two questions: What can we experience in an urban space just by listening? And how do we navigate a route through that world of sound to reach a greater understanding of it whilst also, literally, negotiating our movements through that urban space? The evidence suggests that the TTC Subway Musicians' Programme offers a microcosm of Toronto society. Thus, this underground soundscape can be used to explore IV and identify facets of the city's musical identity and the lives of its inhabitants. Such a study can also offer a new perspectives on how civic environments become established through policy and management schemes which champion the installation of music into urban environments once devoid of music cultures, that is, organised, identifiable sounds, crafted sounds that carry meaning, that are able to lift commuters out of an otherwise auditory jungle (a world of noise with only coded information: the train is now arriving, the doors opening and then closing, I am now leaving the station and getting to my destination). Thus, it is clear that the TTC system is highly complex in terms of its community and cultural relations and its political economy. Moreover, the interweaving of strolls, sound maps, sound clips, sound exercises, and photos within the text, allows for greater opportunity to experience and explore the underground soundscape at specific points in time. Ultimately, this thesis, which one might regard as a sonic mapping of the underground through sound, brings popular music-making and urban geography into soundscape analysis, highlights the role of music in placemaking, and presents a new way of navigating the city.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0160.009
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.030
GPT teacher head0.234
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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