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Record W2143624158 · doi:10.1080/07494467.2014.906698

<i>Audible Ecosystemics</i>as Artefactual Assemblages: Thoughts on Making and Knowing Prompted by Practical Investigation of Di Scipio's Work

2014· article· en· W2143624158 on OpenAlexfundno aff
Owen Green

Bibliographic record

VenueContemporary Music Review · 2014
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersUniversity of EdinburghMcMaster University
KeywordsCraftConversationNegotiationMusicalAestheticsSociologyKey (lock)Social practiceVisual artsArtCommunicationComputer scienceSocial sciencePerformance artArt history

Abstract

fetched live from OpenAlex

AbstractBy exploring Di Scipio's Audible Ecosystemics through the optic of a succession of practical student projects we see that the processes and forces involved in the making can in turn be viewed as an ecosystem. Some key aspects of this revolve around the ways in which technical and social matters interweave in practice—such as negotiating transitions between coding and practising—and how musical identities and design choices can interact. I draw from this the thought that the dynamics of the negotiation between the technical and social are a key aspect of electronic musical craft, but that this topic remains sparsely accounted for in our discourse. I suggest that devising better means of articulating about such negotiations—and about practice more generally—is a way in which practice-led research in this area can contribute usefully to the wider endeavour of musical research.Keywords: Di ScipioEcosystemsPractice-Led ResearchMaking AcknowledgementsMany thanks to the students who have participated in the Emergent Sonorities projects at the University of Edinburgh for being such good sports, and to Agostino Di Scipio for being so generous with his time and materials (to the students as well as to myself).Notes[1] Or perhaps the narrowness of what seems exciting. It could be that mixers, loudspeakers and microphones, for instance, feel like known, predictable entities.[2] An unpublished 2008 recording of Background Noise Study (Di Scipio, Citation2008) also starts against a texture of conversation. As with the performance by the students, it seems to take somewhat longer to settle in to itself. Partly, one may speculate, because the initial amplified sounds are not sufficiently different from the chatter to alert the crowd that something is happening, and partly because the starting sound level is so far above what the system 'expects' that the transitions necessary to trigger transformative processes will be forestalled. Rather than taking this to be problematic, it may be more interesting to view Background Noise Study as being sensitive to socio-musical context …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.295
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2014
Admission routes1
Has abstractyes

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