Listening to Animals: Interspecies Understandings through Performance-Based Research
Bibliographic record
Abstract
We live in an interspecies community, and yet much of the time only human perspectives are acknowledged or considered valid. In order for animal perspectives to be recognized, the mode of inquiry needs to privilege animal ways of knowing and being in the world; it needs to privilege performance, and practice-based research. This paper explores the potential and importance of engaging performance-based research in interspecies contexts. The analysis centers on the performance-based research project, Playing in Silence, which invites musicians to improvise with horses in an open and unstructured space. Playing in Silence demonstrates that through the shared language of performance, humans and horses can co-create understandings, expand knowledges, and learn about one another. Many humans spend an inordinate amount of time speaking about animals, but completely overlook the possibilities of speaking with animals, of learning with and from them, and of discovering their perspectives as unique individuals. However, by tuning into the nuances of another’s performance, through the reflexive inquiry of performance-based research, interspecies understandings can be found, challenging the dominant human-centric perspective of the world, and opening up new realms of understanding.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".