Gender, Affective Labour, and Community-Building Through Literary Audio Recordings
Bibliographic record
Abstract
This article emerged from the “feminist close listening” methodology we devised together during a collaborative listening session in Montreal, December, 2017. We began the practice of listening to recordings together, in real time, as a way of attuning ourselves to the related inquiries that our archives of interest shared. For Karis, this archive is the SoundBox Collection, housed in the AMP Lab at the University of British Columbia, Okanagan Campus, where she serves as Director. For Deanna, this archive is the Roy Kiyooka Audio Archive, housed in the Contemporary Literature Collection at Simon Fraser University. The archives share the same media formats (reel-to-reel and compact cassette tapes) as well as the common generic features of recording spontaneous, candid conversation, often voiced in contexts that are considered domestic, intimate, and private. Our listening sessions aimed to collaboratively outline questions, approaches, and best practices toward this unique subset of literary recordings. The article that follows is one concrete example of how those conversations unfolded.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| 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".