Sensory ecologies: the refinement of movement and the senses in sport
Bibliographic record
Abstract
Sport is centrally concerned with the human body. Those concerns focus on how bodies move materially in space and in time. In this article, we develop our concept of “Sensory Ecology” to elucidate how one might come to develop and understand the creation of specialist bodily knowledge found in sport. Sensory ecologies are produced through the refinement of enskilled movement of bodily materials in specific spatial and temporal confines. To understand the embodied knowledge that athletes learn, it is crucial to ensure the connections between a body and its environs, the body-in-the-world affirmed via sensory interactions, and the information generated from those interactions are maintained in any research on embodiment, being-in-the-world, and the self. The body, its senses, and its surrounding environs simply cannot be separated from one another. Therefore, a sensory ecology sits in these intersectional coming-togethers of space, time, and material made manifest through the sensing of bodily movement. Throughout this article, we discuss the material of bodies and their sensing of spatialities and temporalities by arguing that our concept of “Sensory Ecology” provides a means for exploring the cultural specificities of sensing the moving and sporting body in particular and ways of being more generally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.005 | 0.007 |
| 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".