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Record W7108457696 · doi:10.5281/zenodo.17801104

My Sunset Is Your Sunrise

2025· article· W7108457696 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSunsetMerge (version control)TelematicsImprovisationSunrise

Abstract

fetched live from OpenAlex

In "My sunset is your sunrise, yet we touch," we propose a three-way hybrid-telematic improvisation, by three displaced musicians, featuring 2 DMIs, and an acoustic-prepared piano. The musicians are located in Montreal, Singapore and Canberra at the NIME conference. This performance explores the concept of a multimedia meta-instrument, a collaborative process that integrates audio, video, projection, and lighting into a cohesive whole. The meta-instrument redefines presence and interaction, challenging traditional notions tied to physical co-location. Our approach draws on Karen Barad's concept of touch as an entangled, relational act. Here, touch extends beyond the physical to encompass the mediated interplay of sound, visuals, gestures, and light across networked spaces. In this environment, the tactility of capacitive keys, the resonances of the prepared piano, and the shifting gestural logic of controller-based sound art merge into a dynamic, co-constitutive system. This telematic improvisation demonstrates how the tactile interactions with instruments, the resonances of sound, and the dynamic interplay of light and video create new forms of connectivity and shared expression across a blurring of physical and virtual spaces. Through this meta-instrument, we aim to push the boundaries of telematic collaboration and artistic interaction.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.005
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.004

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 head0.040
GPT teacher head0.269
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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