Listening to Country: Immersive Audio Production and Deep Listening with First Nations Women in Prison
Bibliographic record
Abstract
Listening to Country was an arts-led research project where, as an interdisciplinary team of practitioner-researchers, we worked with incarcerated Aboriginal and Torres Strait Islander women to produce a one-hour immersive audio work based on field recordings of natural environments. The project began with a pilot phase in Brisbane Women’s Correctional Centre (BWCC), Australia, to investigate the value of acoustic ecology in promoting wellbeing among women who were experiencing separation from family, culture, and Country (ancestral homelands). The team facilitated a three-week program with the women, using arts-led processes informed by visual art, performance, Indigenous storywork, and dadirri (deep, active listening). The soundscape presented here is a response to the creative process that we led inside the prison and the audio work that the incarcerated women co-created with the research team. The accompanying text describes the background to the original project, the process we undertook in the prison, and our methodology for translating knowledge from the research based on the acoustic and poetic resonances of our experience.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".