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Record W2118828747 · doi:10.1109/icme.2006.262935

Adaptation Scenarios for New Media Artworks

2006· article· en· W2118828747 on OpenAlexaff
Anis Ouali, Brigitte Kerhervé, Odile Marcotte, Paul Landon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAdaptation (eye)Computer scienceMultimediaEvent (particle physics)Focus (optics)Action (physics)Media artsDigital mediaHuman–computer interactionWorld Wide WebVisual artsArt

Abstract

fetched live from OpenAlex

Artists in new media arts explore the possibilities offered by digital technologies to create adaptive and interactive new media artworks integrating audio and video. In this paper, we are interested in adaptation mechanisms for such artworks and we focus on the design and implementation of adaptation scenarios. We formalize adaptation scenarios by using the event-condition-action paradigm and we introduce the concept of adaptation policy. We present the adaptation engine we have designed and prototyped to manage and execute adaptation scenarios

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.064
GPT teacher head0.325
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

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 designTheoretical or conceptual
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

Citations1
Published2006
Admission routes1
Has abstractyes

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