Leveraging Technology to Facilitate Access: Automated Description of the Mariposa Folk Festival’s Born- Digital Performance Recordings
Bibliographic record
Abstract
In recent years, the increasing volume of born-digital materials (i.e., those created digitally rather than digitized from analog originals) deposited in archives has fostered the development of new software-based tools and workflows for processing archivists. Archivists seeking practical guidance for preserving digital materials have a wealth of resources at their disposal, including many community-owned tools, workflows, and tutorials. This case study examines how archival standards and technological advances have influenced the semi-automated description of born-digital audio records through the lens of a recent project at the Clara Thomas Archives and Special Collections (CTASC) at York University Libraries (YUL). The Mariposa Folk Foundation Fonds, containing a large and growing collection of born-digital audio recordings, served as an opportunity to design and test a new software-aided descriptive workflow. The project leverages the programmable nature of born-digital materials in an attempt to streamline the time-consuming process for creating the item-level descriptions typically associated with sound recordings and born-digital records while also improving the discoverability of this material in the unmediated environment of online finding aids. This case study demonstrates how technology has influenced descriptive practices, with the advent of online finding aids providing increased access to archival descriptions, online databases permitting keyword searching, and tools to script metadata extracted from born-digital records enabling robust archival descriptions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".