How Not to Spill Coffee on Your Tapes. Best Practices for Preserving Oral Archives
Bibliographic record
Abstract
Oral sources are extremely fragile, but valuable research objects that are worth preserving. However, this often proves much more demanding than preserving other historical sources, such as artefacts or written documents, since their preservation relies on specialised infrastructure. To inform the oral history scientific community of developments and available resources that can support oral history researchers in their work, SSHOC and CLARIN ERIC recently teamed up to deliver a webinar, titled “How Not to Spill Coffee on Your Tapes. Best Practices for Preserving Oral Archives”, under the framework of CLARIN Cafés. The talk features two presentations: · Presentation: Two ethnomusicological collections in French museums and the Michel Seurat sound archives catalogue recorded in Lebanon and Syria in the 1980s - as reported in recent issues of Sonorités, the open access journal of the French Association of Sound, Oral and Audiovisual archives (AFAS) - Véronique Ginouvès CLARIN-FR · Presentation: The Italian Vademecum, a two-year collaborative enterprise to offer Italian scientific communities and non-academic stakeholders an online oral preservation catalogue designed as a guide to preserving and archiving oral resources for potential enhancement and re-use - Silvia Calamai University of Siena, CLARIN-IT
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.068 |
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".