Bibliographic record
Abstract
This article explores the development of poetic Audio Description (AD) in live dance performances, with a focus on the Canadian context. Poetic AD embraces metaphor, sensory language, and embodied descriptions, enhancing accessibility while expanding the artistic potential of AD. Through a reflection on past experiences and the recent Languaging Dance project, this paper examines creative and collective approaches to live AD that emphasize embodied, sensory, and poetic language. By integrating improvisational and artistic techniques, poetic AD emerges as both an inclusive and aesthetic form of engagement with dance. This research contributes to broader discussions on inclusive arts practices, accessibility aesthetics, and multisensory engagement in live dance AD. Lay summary This study looks at how poetic language can make Audio Descriptions (AD) more meaningful for Blind and Low Vision (BLV) audiences during live dance performances. The AD approach presented goes beyond simply adding accessibility after the show is done; it helps make dance performances richer and more inclusive for everyone involved from the start. As such, the article suggests that involving BLV artists in creating AD can improve the descriptions. The article looks at the Canadian context and using reflections from Languaging Dance, a research project that took place in 2023-2024, it shows that poetic AD can bring dance to life through vivid, poetic language, making it a more engaging experience.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".