"Revolutions in Sound": Keynote Duet
Bibliographic record
Abstract
The authors were honoured by the invitation to present a dialogic duet keynote for the Revolutions in Sound conference beautifully imagined by Caitlin Marshall, Patricia Herrera, Marci McMahon, and Iván Ramos. Their collective generosity included not only food, travel and an honorarium but also some much-needed intellectual sustenance: they provided us with several prompts to work with for our remarks. You will find their questions in bold and our respective answers to follow. It was a moving experience of turn-taking in the live, of singing in round, rather than a masterful display of individualistic pontification. We would like to extend our infinite thanks for the organizers’ thoughtful questions and for giving us such rich soil to plant future idea seedlings. For many of us, this was our last moment of being with others before the isolation of the pandemic took hold, and it is partly for this reason we have left our remarks mostly unedited. Here they are published very much as they were delivered in the live so as to record that moment of togetherness and also remind of a polemical mode that is possible when in an actual room with others you can see, hear, and feel. There is thus a palpable sense of safety-in-experimentation in our remarks.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".