Amplified Listening to Race and Gender in Fiamma Montezemolo’s "Echo" and Stephanie Dinkins’s "N’TOO"
Bibliographic record
Abstract
Looking retrospectively and prospectively, this article reflects on what is heard when listening to the women talking inside artworks. Both Echo (2014) by Fiamma Montezemolo and Not the Only One (N’TOO), an ongoing project by Stephanie Dinkins, present sound archives that animate spaces with fragments of dissenting women’s voices. Looking back at the impact of inSITE, a curated international art festival in the Tijuana and San Diego region, Echo amplifies the sonic remains of art produced in the context of NAFTA. In N’TOO a living AI archive is potentiated as a fourth generation in the artist’s family, carrying on the histories and subjectivities of three women. N’TOO optimistically intervenes in AI’s expansionist and biased trajectories by furthering Black and familial subjectivity in a specific context of racial and gender foreclosures. Despite their spare use of sound, both artworks enable amplified listening through the avatar-like objecthood afforded by gallery-based media artworks. This methodology is consistent with decolonizing efforts in Canada and has significance for artists intervening in intersectional, race, and gender studies, border studies, and in settings of computational or interactive technology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".