Aural Histories: Sound as Language in the Work of Mani Mazinani and Sanaz Mazinani
Bibliographic record
Abstract
Abstract What does our environment sound like, and who and how does it include or exclude? Who hears what, and how do we hear differently? This article examines how the brother-sister artist duo Mani Mazinani and Sanaz Mazinani asks viewer-listeners how they listen and what they hear, shifting Western-oriented perceptions of noise, sound, or music. While each artist has pursued their own, multifaceted artistic practices, the siblings come together through collaborative practices, with the collective experience of migrating from Iran to present-day Canada at young ages imprinted on their art. The article focuses on three of their aural-visual projects: What Language Are They Speaking? (2016), Shift (2018), and Dastgāh (2024), following their shared trajectories and individual paths, together exploring the themes of movement and migration, sound as language and vice versa, and the politics of perception. Reading from sound studies and the visual arts, the article looks at how the idea of difference is embedded in their artworks, and how they ask viewers to open their ears to listen differently, to unsettle settler-colonial perceptions of listening and sound.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".