Bibliographic record
Abstract
The core question that this chapter examines is how music technology in, for, and as music education should be assessed in teaching and learning contexts. Commencing with an explanation of the concept of the personal best in the context of running culture, it suggests that this approach to assessment, which incorporates self-assessment and peer-assessment, ought to be used in music education settings. The chapter then presents a rationale for delimiting the definition of “music technology” as a means of making music, before proceeding to discuss the theories of assessment that provide a framework for suggesting ways of realizing learners’ personal bests. The chapter argues that peer feedback and self-feedback are synergistic strands of the feedback loop that learners must enter to experience an authentic and complex learning environment, and that summative assessment can and should be a natural outgrowth of formative assessment. Ultimately, the aim of this approach is to construct a context in which learners of all levels and abilities can engage in meaningful experiences with music technology while providing a framework to evaluate the quality of the learning that has taken place from multiple perspectives. If teachers and learners commit to this iterative process of assessment as learning, one in which they start but do not stop, then they will have entered the feedback loop.
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 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 | 0.023 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".