Unboxing the Archive with SpokenWeb UAlberta: A Case Study in Literary Audio Rights
Bibliographic record
Abstract
The following paper builds on recent theorizations of ethical archival methodologies, using a case study of archival "unboxings" at the SpokenWeb UAlberta as an invitation to think through critical questions such as these: How do we be good caretakers of audio data, aural/audio histories? Who are the stakeholders represented in the collection, and also, what is at stake, not just in terms of our legal obligations, but our ethical and moral responsibilities? The paper explores how digitizing historical audio collections can create opportunities to open up a dialogue between scholars and artists, but at the same time also introduce new complexities around issues of privacy and consent. As we have found, “care-full” archival work, to use Cowan’s term, requires us to engage with and imagine past, present, and future media, as well as past, present, and future users. Le présent article s'appuie sur des théorisations récentes des méthodologies archivistiques éthiques, en utilisant une étude de cas des "déballages" d'archives chez SpokenWeb UAlberta comme une invitation à réfléchir à des questions critiques telles que : Comment pouvons-nous être de bons gardiens des données audio, des histoires orales/audio ? Quels sont les parties prenantes représentées dans la collection, et également, quels sont les enjeux, non seulement en termes de nos obligations légales, mais aussi de nos responsabilités éthiques et morales ? L'article explore comment la numérisation des collections audio historiques peut créer des opportunités pour ouvrir un dialogue entre chercheurs et artistes, tout en introduisant de nouvelles complexités liées aux questions de confidentialité et de consentement. Comme nous l'avons constaté, un travail archivistique "plein de soin", pour utiliser le terme de Cowan, exige que nous nous engagions avec et imaginions les médias du passé, du présent et du futur, ainsi que les utilisateurs du passé, du présent et du futur.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.039 | 0.029 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".