Bibliographic record
Abstract
Assessment policy, whether explicitly or implicitly expressed, is defined for this chapter as any set of principles or guidelines constructed for the purpose of bringing consistency and fairness to a course of action involving measurement, evaluation, or growth related to any dimension of an individual’s learning. Assessment policies are influenced by time and place and often are shaped by powerful policy frameworks, that is, the political environments in which policy is conceived. The purpose of this chapter is to examine several issues (the status of music as a basic/core/well-rounded subject and the jurisdiction for the teacher certification process) and trends that impact these issues (the decreasing federal government involvement in education; the extension of educational agendas across boundary lines; and the growing power of arts advocacy groups) that emerge at the intersection of assessment policy and music education from a North American (delimited to Canadian, Mexican and US) perspective.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".