Walking the Talk: Three Language Educators Engage in a Walking-Based Art Inquiry for Anti-Racist Education
Bibliographic record
Abstract
As language and art educators committed to anti-oppression, we sought to explore how walking and art-making help us reflect, inquire, create, and act, upon new understandings of anti-racist education. Living in three different cities (Edmonton, Vancouver, and Palermo), we collaborated on a walking-based art inquiry for ten weeks in the summer of 2020, combining walking, art-making (photography, painting, mixed-media collage, screenprinting, and poetry), reflecting, and discussing. We were curious to investigate, both individually and collaboratively, what an anti-racist curriculum looks and feels like to us, and what walking and art-making might do in the process of learning and teaching. We situated our project in an arts-based research paradigm (Conrad & Beck, 2015), and we were inspired by Feinberg’s (2016) walking-based pedagogy and Judson’s (2018) walking curriculum. This article presents artistic experiments we created, as well as curricular insights that emerged from our process, for example, that walking and art may serve as dehabituating forces to help us openly feel, question, protest, and reimagine education, from intersecting perspectives of race and language.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.041 | 0.024 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".