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Record W4242292944 · doi:10.14236/ewic/eva2017.51

Berlin Remix

2017· article· en· W4242292944 on OpenAlex
Jim Bizzocchi, Arne Eigenfeldt, Philippe Pasquier, Jianyu Fan, Le Fang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueElectronic workshops in computing · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMetadataComputer scienceGenerative grammarFunction (biology)Shot (pellet)EmotiveMultimediaWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

Berlin Remix is a demonstration artwork using the "Dada Processor", a generative documentary system being developed by the Generative Media Project at Simon Fraser University in Canada. The system will use encoded rule sets - called "style sheets" - to sequence and emit an ongoing series of short but distinct documentary "films" drawn from a database of video clips. Each clip has associated metadata tags that indicate the visual content, shot length, and emotive value). The style sheet rules will use this metadata to guide the construction of the sequences for each "film" that is emitted. The system is generative, and will require no user interaction to function. The Dada Processor video sequencing system works in tandem with a generative music system ("MuseBots") (Eigenfeldt 2016). Future versions of the work will be coupled with a generative soundscape system ("Audio Metaphor") (Thorogood & Pasquier 2015).

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.558
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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