Walking Together in a Pandemic: Reflections on a Semester of Place, Decolonization, and Classroom Community
Bibliographic record
Abstract
In this co-authored reflection by seven graduate students and one instructor, we revisit the “Apocalyptic Feminisms” graduate class taught in Fall 2020, at the height of the pandemic, in which students were asked to engage with interdisciplinary writing on the Anthropocene and to hone their own feminist praxes through going on and documenting their walks. We reflect on the “Walk and Movement Reading Instagram Engagements” assignment, exploring what walking can offer to deepen theoretical, methodological, and praxis-based approaches to environmental and place-based learning. We do this by writing from a cascading voice approach, where each reflection stands on its own and together builds into a whole greater than its parts. Following on an introduction to the course and assignment, we begin with a conceptual outlining of the meanings of place, decolonization, and academic community. Next, each of the graduate student collaborators responds to the following prompt, “How did the walking project on IG [Instagram] influence how you thought about questions of place, decolonization, and classroom community during a pandemic?” Each co-author develops a robust engagement with this question drawing on their own pandemic Fall 2020 semester, their photographs from the course, and the insights and practices they developed in conversation with each other throughout the span of the semester. Together, this reflection offers thoughts on the feminist and place-based utilities of walking pedagogy as well as, through collaboration, challenges the very way in which academic scholarship is done at the site of the late capitalist academy and its topos of success, competition, and meritocracy.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".