Bibliographic record
Abstract
As a professional bassist who identifies as female and queer, I have my share of horror stories about experiences in the music industry. But I also have a solid peer group that is actively working to improve things, and with whom I have an ongoing dialogue. Specifically, in the last year, we started to observe the radical behavioural changes we've made as a society in response to the Covid19 pandemic, and how these protocols could be viewed with an anti-oppressive lens. I started to think about my values as an improvising musician, and how they provide an analogy and a framework for broader social interaction. I used the writing of this article as an opportunity to speak with some of my musical peers about their individual experiences and their ideas for creating safer spaces. We talked about the skills we had as improvisers, and how the pandemic could be a pivot point in creating a safer, more authentically inclusive music scene for women, trans, and gender queer people. This piece reflects those conversations and offers practical considerations and theoretical frameworks that are relevant to individual improvisers and ensembles, as well as promoters, curators, and venues.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.078 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 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".