Introduction: Sounding Justice in Pandemic Times
Bibliographic record
Abstract
This essay introduces a large and diverse special issue on ‘The Acoustics of Justice: Law, Listening, Sound’. Until recently the acoustic dimensions of law and justice were not a major concern in the academy, either in self-consciously legal scholarship, or elsewhere. Things are changing, as indeed the size of this collection suggests. And our hope is that the work gathered here will go some way to addressing this deficit. Nevertheless, this introduction does not attempt to theorise how. Though the collection was conceived in 2019, it was mostly produced since the arrival of COVID-19. And we are tired. We have been working from home too long; or rather our homes have been ‘requisitioned’ for work. Our teaching loads have increased and the teaching itself rapidly onlined. All while separated from loved ones and attempting to school or otherwise entertain our kids at home. And we are the lucky ones. This introduction is, in a sense, an anti-introduction, or a non-introduction then: a strategic withdrawal of labour that opens the door onto the collection but offers little in the way of guidance to readers once they step through. Whether that is generous or irresponsible we leave to readers to decide.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".