Oral History and Open Access: Fulfilling the Promise of Democratizing Knowledge
Bibliographic record
Abstract
The Archives of Lesbian Oral Testimony (A LOT) is an online digital archive of lesbian oral/aural testimonies housed at the Simon Fraser University Library. A LOT began as a simple preservation project. Many people who interviewed women in the 1980s and 1990s about their experiences as lesbians had not archived their analog tapes. I had access to a lab and funding that would allow me to convert analog tapes to .wav (digital sound) files, and it was this modest task I set out to do in 2010. The deeper I read into the literature on digital humanities, however, the more complex and exciting the project became. In this note I present the ideas that have shaped my thinking about building this archives, the concept for the archives that has evolved, and our overall objectives.
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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.034 | 0.057 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".