Design Sprints and Direct Experimentation: Digital Humanities + Music Pedagogy at A Small Liberal Arts College
Bibliographic record
Abstract
In this essay, we detail the pedagogical collaboration between a digital humanities librarian, a professor of music and digital media, and a second-year music student that took the form of a design sprint. The product of the design sprint was the Mapping Sentiments through Music (MStM) application. Using this project as a case study, we argue that both digital humanities and music education share a commonality: both disciplines can incorporate elements of design thinking to be successful. As a result, our efforts center direct experimentation with a team, and foster design thinking by promoting descriptive exchange, creative problem solving, and the creation of emergent rather than explicitly delimited meanings. We conclude with several remarks on overlaps between music and design pedagogy, and on librarian-faculty collaborations.
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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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".