The Privilege of Not Walking Away: Indigenous Women’s Perspectives of Reconciliation in the Academy
Bibliographic record
Abstract
The release of the 2015 Truth and Reconciliation Commission (TRC) report titled “Honouring the Truth and Reconciling for the Future” has evoked a persistent call within learning institutions to Indigenize education, decolonize systems of power, and reconcile Indigenous–settler relations and knowledge. Within this context, the TRC’s “Calls to Action” are frequently invoked by institutions attempting to achieve just action. While reconciliation remains a complex, political, and settler-driven endeavour, there has been an effort to “fill the gap” with Indigenous presence, knowledge, and students within academic institutions. Given the limited research on the gendered aspect of reconciliation, our paper contributes to this conversation by examining the impact of the “filling effort” on our critical community work and the ways in which we as Indigenous women engage in reconciliation. By this, we mean the ways we live and understand reconciliation by looking inward toward each other as women, to learn from each other, and to lift each other up. Through a relational accountability methodology and mixed methods (Wilson 2008), we draw strength from our relational and resurgence approaches in an effort to capture our commitment, challenges, and transformative vision of reconciliation as Indigenous women in the academy.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.053 | 0.097 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| 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".