Bibliographic record
Abstract
Created by a team of literary scholars, students, and a librarian, Orlando: A Podcast on Womenʼs Writing comprises twelve informal interviews that synthesize and share scholarly work on womenʼs writing from medieval times to the present, with capacious definitions of “women” and “writing.” The podcast is closely connected to Orlando: Womenʼs Writing in the British Isles, from the Beginnings to the Present (Cambridge UP, 2006–), a textbase of original scholarship on womenʼs writing encoded with a bespoke XML tagset. Examining the interplay between the podcast and textbase within our broader scholarly landscape, this audio essay focuses on issues inherent in translating knowledge between written and oral forms in the podcast lifecycle, from the process of creating a lively and accessible scholarly interview to the difficulties of transcription. This discussion mirrors the content of the podcast: feminist critics like Diane Watt, whom we interview about medieval writer Margaret Paston, have challenged the idea that women needed to read the written word in order to be considered literate, arguing that dictation could constitute an alternative form of literacy. By challenging text-based representation as the primary legitimate form of scholarship, we foster a more inclusive view of scholarly communication that speaks to the existing tradition of multimodality in feminist writing. Providing insight on the affordances of the podcast medium and library partnerships for advancing the aims of feminist scholarship and the preservation of new forms of scholarship, the essay explores our reflections on networked knowledge production and multimodal representation, which make scholarship accessible in and beyond academic contexts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".