Experiential Learning as Archival Activation
Bibliographic record
Abstract
Music archives hold unique value in understanding the process of creators and sparking creativity in new researchers through their exploration. The University of Calgary Archives and Special Collections preserves and shares the archives of prominent composers, record labels, musicians, and music historians. In 2024, three colleagues from University of Calgary Libraries and Cultural Resources were awarded funding from the Taylor Institute for Teaching and Learning to initiate an archives student-in-residence program. Through purposeful connection with archives, students, archivists and librarians, the project’s goal is to investigate the use of the archive as a site for experiential education. This student-in-residence program invites three students over the course of three years to critically explore, analyze, synthesize, interpret, and activate three prominent music archival fonds: Norma Beecroft, Edith Fowke, and Melvin Crump. The principal investigators will work with the student residents through a process of co-inquiry to support them through the archival research process and applying creative approaches to the rich and varied archival materials maintained by Archives and Special Collections. Through multiple iterations of residencies, one every year over the course of three years, team leads will be able to move beyond a single context and look for patterns that emerge from the collected experiences. This article explores and reflects on the first year of the project focused on the Norma Beecroft fonds and explores the goals of the long-term project into the coming years.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".