Hooping through Interdisciplinary Intertwinings: Curriculum, Kin/aesthetic Ethics, and Energetic Vulnerabilities
Bibliographic record
Abstract
Learning to become a teacher is inherently stressful. Daunting deadlines of final assignments become the curricular hoops students jump through, conceptualized as gateways to experiencing something meaningful on the ‘other’ side, beyond the circumscribed constraints of a university campus. In an ethic, kinaesthetic, and energetic pedagogical response, teacher candidates were invited to spend time with and physically explore the very object they associate with their exasperations: the hoop. This inquiry thus aimed to explore emergent interdisciplinary understandings between the practice of ‘learning to teach’ and ‘learning to hoop’ on campus and with children in local schools and a First Nations community. Student interviews revealed that the practice of hooping not only released stress, it afforded an opportunity to loosen rigid notions of curriculum and pedagogy, specifically that learning is more than a linear journey of jumping through a prescribed set of hoops and that teaching is more than a process of transmitting information. A bodily pedagogical practice of vulnerability, fluidity and interactivity thus emerged as teacher candidates became receptive to step into and be transformed by the hoop.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".