Queer Coding the Audio Archive: Linked Data and the Lesbian Organization of Toronto (LOOT) Oral History Tapes
Bibliographic record
Abstract
This paper was presented at the SpokenWeb Symposium 2023: Reverb: Echo-Locations of Sound and Space. Is metadata a “literary audio event?” The Lesbian Organization of Toronto (LOOT) Oral History Tapes were discussed as a contribution to SpokenWeb, because they enhance 2SLGBTQIA+ content in the metadata from literary events. The oral history tapes of this collection are restricted; therefore, the main goal of this work is not necessarily to make the files public, but to develop a methods approach to working with descriptive metadata of sensitive files. We hope the project will serve as a case study of ethical data practices that can then be shared with 2SLGBTQIA+ community members, wider researcher communities, archivists, and librarians about how to work with the nuances of digitization and access to sensitive material in historical context. The LOOT Oral History Project interview tapes were recorded during 1988-1990 by sociologist Becki Ross and are extensively quoted in The House that Jill Built. Each of the interviews provides a unique perspective on LOOT’s four-year existence (1976-1980) and the politics of a particular Lesbian community located in Toronto (Ross 1995), that overlaps with poetic and publishing communities in the Spoken Web network. What are the ethics of making these overlaps visible through metadata work, even if the content of the tapes must remain restricted? This paper details the technical approach used to digitize and describe these analogue audio tapes according to archival standards and to the Spoken Web metadata schema. A Data Management Plan was key to documenting our procedures for respecting ethical guidelines (Morissette et al 2021).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".