Hitting the right note: developing an archival appraisal strategy for musicking in Manitoba
Bibliographic record
Abstract
Musicking is to take part in the creation of music, as defined by musicologist Christopher Small. Whether by performing, listening, producing, or organizing, musicking encompasses all of the activities that surround making music. This shift to addressing the activities of music-making, and not the music itself, is similar to the modern approach to archival appraisal where it is not the records themselves that are appraised, but rather the activities of their creator. By applying Small’s term to making music, a wider lens in which to evaluate the archival value of music records is established. Through that lens this thesis identifies the functions of musicking to be considered when appraising and acquiring archival records, places those functions within the larger Canadian society for context, and examines particular archival collections in Manitoba as a case study to begin developing a strategy in which Manitoba’s musicking records can be preserved for future generations.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.038 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".