User experience with digital musical instruments: a transferable method for longitudinal evaluation
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.
Bibliographic record
Abstract
We present the development and demonstration of a transferable method for studying user experience (UX) with digital musical instruments (DMIs) over time. We introduce DMIs, music interaction, stakeholders, and observable experiential aspects of the user-instrument relationship (UIR), grounding the development of our method in theoretical frameworks from human–computer interaction and music technology. We discuss structured evaluation strategies for studying evolving experiential components of the UIR over time, noting the limitations of current approaches. We describe the development and structure of our method before reporting on the initial execution of the method in a limited context. Using a small sample of individuals with diverse musical backgrounds and a compressed time period, we demonstrate how the method can be used to collect rich qualitative data on dynamic aspects of the UIR with an unfamiliar DMI. Results from this initial demonstration suggest that the method is able to capture comparable experiential data from different perspectives and that participants’ backgrounds played a central role in their emotional and cognitive experience. We reflect on the limitations and successes of the demonstration, and offer specific suggestions for expanding the method in future, to more widely assess its transferability to different DMIs, participants, and real-world musical contexts.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it