Bibliographic record
Abstract
In this paper, we propose embodied listening as pedagogical praxis in which we are receptive to how our whole bodies are involved in communicating with each other. Embodied listening disrupts what we call “speech-as-presence”—normative expectations of student participation emphasizing verbal contributions and privileging particular bodies. These expectations contribute to the reproduction of oppressive logics at work in classrooms—racism, hetero-patriarchy, white feminism, masculinism, ableism, colonialism. We argue that embodied listening can serve as a source of knowledge about these logics, supporting transformation of classroom expectations beyond imposed norms. We reflect on our experiences developing embodied listening practices in our undergraduate courses through our observations and students’ own reflections. Our findings demonstrate both the transformative potential of listening in classrooms and the tensions produced as these strategies discomfited students and disrupted classroom norms. Finally, we engage with critical perspectives on listening positionality from Indigenous studies, disability studies, and sound studies towards deepening our understanding of differences and multiplicities in how we listen. We illustrate how we continue to develop ways to incorporate this work in our classrooms and support students in the exhaustive and uncomfortable work of embodied listening and imaginative ways of being in the classroom.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".