Writing as righting: Truth and reconciliation, poetics, and new geo‐graphing in colonial Canada
Bibliographic record
Abstract
This paper is anchored in two recent and concurrent openings, openings that offer opportunities for geographers to consider new modes of engaging colonial violence. The first opening is the release, in Canada, of the Truth and Reconciliation Commission's final report and calls to action. By demanding new types of settler‐subject attention to Indigenous peoples and places, it opens new spaces for extending reflection about anti‐Indigenous racism and colonial violence in Canadian consciousness. The second opening is geography's growing uptake of creative and humanities‐informed theories and practices. These manifest in new knowledges and practices with consequent possibilities for addressing colonial violence. I consider these two openings first by proposing changes to conversations about settler‐normalized violences lived by Indigenous peoples, and, second by engaging poets working to radically re/configure language and written expression. Specifically, the paper ends with a call for geographers—particularly non‐Indigenous settler geographers—to rethink ways (and forms) by which we produce knowledge, especially about colonialism and Indigenous geographies and especially in and through writing practices. The paper is experimental in form, meant to disrupt easy uptake or digestion of ideas that must remain—for settler subjects—fundamentally ragged, upsetting, and always beyond conclusion, coherence, or closure.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.045 | 0.074 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".