Place-based embodied pedagogies: Implications for teaching Indigenous presence in Tiohtià:ke/Mooniyang/Montreal
Bibliographic record
Abstract
This article employs Indigenous urbanism as an analytical approach, as developed by Anishinaabe and settler scholar Heather Dorries (2023), to show how pedagogical interventions employed in the teaching of an undergraduate course at Concordia University (Montreal, Canada) contributed to an enhanced theorization of the city. It discusses the ways in which pedagogical activities shaped the students’ understanding of historiography, Indigenous urban lives, and the construction of shared urban spaces. In focusing on the local histories, territorialities, and specificities of Montreal as a shared and continuously renegotiated Indigenous-settler space, pedagogical interventions used in the course prompted students to reflect on how their own positionality coproduces knowledge about the city. Understanding themselves as knowledge makers, and thus co-producers of urban spaces, students were able to better define the contours of their own relations to the Montreal urban spatialities and socialities. By generously sharing their evolving meaning-making and positionalities, students demonstrated that the Right to the City is a collective reclamation of the urban space that recognizes and affirms Indigenous peoples as rights holders and not simply stakeholders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".