Becoming Horse in the Duration of the Moment: The Trainer's Challenge
Bibliographic record
Abstract
Language skirts the somatic fringes of the moment, particularly in practices where the powers of human speech and writing seem nullified. Horse training is one such practice. We tell stories of horse training that sensitize us and bring us close to creatures whose movements, resonating with our own, connect us to a prelinguistic, animate world. In so doing, we bridge the gap between the reflective detachment of our customary, wordy practices and the wordlessness of pre-reflective animality. Yet a phenomenological discursiveness shows us how vital moments of “becoming animal” can be consciously and linguistically sustained. “Becoming horse in the duration of the moment” addresses the corporeally-charged consciousness of being with horses on the ground and in the saddle. This paper describes a relationality and temporality that, though mostly wordless in strictly human terms, speaks a sophisticated language of moment resonance. In so doing, it contests the dualisms of verbal and non-verbal discourses, the separation of humans and other animals, and the divisions that keep somaticity on the fringes of consciousness. It responds to the ecological challenge to get beyond the linguistic appropriation of the other, human speciesism and anthropomorphic projections, in order to discern the kinesthetic and energetic expressivities of connecting with other beings and with the elements of animate existence. Horse training provides a case for “living” in the somatic fullness of the moment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".