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Record W2057906018 · doi:10.1145/1496984.1497002

Baroque Baroque revolution

2008· article· en· W2057906018 on OpenAlexaff
Jennifer Jenson, Suzanne de Castell, Nicholas Taylor, Milena Droumeva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsSimon Fraser UniversityYork University
Fundersnot available
KeywordsBaroqueOmnipresenceMusicalComputer scienceVisual artsMultimediaArtEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This paper documents the design and development of a Flashbased Baroque music game, "Tafelkids: The Quest for Arundo Donax", focusing on the tension between constructing an online resource that an audience aged 8--14 would find fun and engaging, and the directive to include historical information and facts, as well as convey some of the sounds, musical structures and conventions of Baroque music, history and culture through play. We begin by contextualizing the game as a collaboration between our team of university-based researchers and the Tafelmusik Baroque Orchestra, two groups with quite different histories -- and understandings - of educational media design. We introduce the problem of how to go about creating a media artifact that would "make public", in a compelling and playable way, key features of Baroque music. We then describe a design process in which we tried to bridge the representation of "expert knowledge" about Baroque music with some of the mechanics used in popular music-based games. A discussion of these particular challenges in designing a bridge from propositions to play, in effect digitally remediating, Baroque music education, concludes by addressing the broader epistemological question of what and how we may best learn, and learn best, from games and play.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.041
GPT teacher head0.323
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2008
Admission routes1
Has abstractyes

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