Bibliographic record
Abstract
Despite increasing attention to the preservation and development of sound archives in academic research and cultural heritage institutions, they are yet to be more substantially embraced in larger theoretical debates on archival theory and practice. Fraught with contested histories through the legacy of ethnomusicology, rooted in the enterprises of colonial imperialism, now in the era of mass digitization and distribution, many sound collections are attempting to develop ethical and empowering methodologies that support community involvement and a vigorous remediation between sound and visuality. Addressing this confluence of concerns, this article considers the ways in which contemporary digital sound archival projects are encouraging an engagement with cultural history and memory in innovative and complex ways, mobilizing the affordances of digital tools and community-based support material with careful attention to the negotiation between its sonic and visual constituents. Through an analysis of two case studies – The Roaring ‘Twenties and Smoke Signals Radio Show Archive – this article examines how contemporary digital archival projects activate and remediate sonic documents and their contextual counterparts to invite a diverse, multifaceted, and multi-sensory encounter with history, memory, knowledge, and the past.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".