On Stones, Ethical Failures, and Epistemological Wormholes: A Conversation between Suzanne Kite, Jennifer Biddle and Florencia Marchetti
Bibliographic record
Abstract
This three-way conversation reflects on practice-based research and the thinking-making of artist, composer, and academic Suzanne Kite (Oglála Lakȟóta), in the context of a Social Sciences and Humanities Research Council of Canada (SSHRC) transnational research project on sensory new media. Departing from Kite’s award-winning collaborative article ‘Making Kin with Machines,’ the sculpture Ínyan Iyé (Telling Rock) and its different iterations, the dialogue explores questions related to ethical and epistemological engagements with humans and non-human beings, including family members, machines, stars, and stones. What does Kite’s Oglála Lakȟóta inspired ‘listening without ears’ mean and do? What can this kind of listening teach us about the relationships between humans and technologies? What critique of otherwise assumed universal or neutral ethics of machine-based learning and digital models of knowledge come from her listening to and creating with non-human beings? How do these practices speak to current debates on Indigenous Data Sovereignty? The article speculates on transformations in sensory and embodied primacies of ontologies of perception in Kite’s thinking-making practice, offering clues as to how differential capacities for engaging with AI, digital knowledge, and machines in hyper-localised modalities might emerge.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.090 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".