Dynamic Systems for Humanities Audio Collections : The Theory and Rationale of Swallow
Bibliographic record
Abstract
This paper approaches a system that has been designed, and continues to be in development, for the aggregation of metadata surrounding collections of documentary literary sound recordings, as an object for theoretical and practical discussion of how information about diverse collections of time-based media should be managed, and what such schema and system development means for our engagement with the contents of such collections as artifacts of humanist inquiry. Swallow (Swallow Metadata Management System 2019), the interoperable spoken-audio metadata ingest system project that is the boundary object for this talk, emerged out of the goals of the SpokenWeb SSHRC Partnership Grant research network to digitize, process, describe, and aggregate the metadata of a diverse range of sound collections documenting literary and cultural activity in Canada since the 1950s. Our talk, collaboratively written and delivered by a literary scholar and critical theorist, a digital projects and systems development librarian, and a library developer / programmer, outlines 1) a theoretical rationale for the audiotext as a significant form of data in the humanities, 2) consequent modes of description deemed necessary to render such data useful for humanities scholars, and 3) a rationale for the development of a specific form of database system given the material and systems contexts that inform our national holdings of documentary literary sound recordings at the present time.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.013 | 0.040 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".