Baroque Baroque revolution
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".