Bibliographic record
Abstract
This Soundscape was a co-creation by Aymeric Vildieu, a blind DJ from France; Janice Rieger from QUT, Australia; Megan Strickfaden, a Visiting scholar from Canada, design students and artists who are blind. This Soundscape was created with the painting Present Portal by Catherine Parker in collaboraton with the QUT Art Museum for the Vis-ability exhibtion. Bringing together a selection of recent acquisitions from the QUT Art Collection, Vis-ability has been conceived as a project to broaden understanding of the lived experiences of people who are blind and people with low vision. Drawing on research in QUT’s Creative Industries Faculty,Vis-ability presents key recent acquisitions to propose alternative ways of engaging with the Art Collection and to consider how technologies can deepen our understanding of vision and challenge our sight-driven experience of art. Through an extensive and intuitive consultation and research process with audiences who are blind or have low vision and experts in the field, Dr Janice Rieger and her colleagues worked closely with QUT Art Museum curators and staff to reinterpret select works from the QUT Art Collection. Artworks have been translated through alternative mediums such as tactile and audio experiences, offering fresh sensory approaches to the experience of colour and patterns.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.652 | 0.332 |
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".