Time as an instrument of settler evasion: Circumventing the implementation of truth and reconciliation in Canadian geography departments
Bibliographic record
Abstract
In 2015, the Truth and Reconciliation Commission (TRC) of Canada released its final report on the Indian Residential Schools system and issued 94 calls to action. Education was identified as core to the reconciliation process. Universities across the country responded swiftly, acknowledging the calls as urgent and long overdue. Institution‐wide task forces were established, and glossy reports were produced with directives to faculties and departments. Given Geography's historic and ongoing implication in white settler colonialism, Geography departments were in unique positions to surface the truths, engage in healing, and reconcile their relationships to Indigenous Peoples and the Land. This paper presents findings from an exploratory case study that sought to understand precisely what Canadian Geography departments have been doing to operationalize the TRC's calls to action in the five years since the TRC report was released. Using Foucauldian discourse analysis of semi‐structured interviews with Geography department heads, we show how settler‐colonial space‐time geographies were often used as a scapegoat to circumvent responsibility at the department level. We are calling on Geography departments to take time away from their standing state of affairs to strategically, structurally, and systematically operationalize the calls to action.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.060 | 0.068 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".