Bibliographic record
Abstract
The resilience of Indigenous people is evidenced throughout the cultural landscape of Canada and emerges in the stories we share about our challenges and how we have overcome them. In the movement to decolonize and indigenize institutes of higher education, are we considering how Indigenous perspectives on resilience are actualized through the experiences of Indigenous faculty and Indigenous students? What is the source of resilience in the current context of Indigenous communities? Is the common definition of resilience, as the ability to bounce back from harm, insufficient in capturing the ways that Indigenous resiliency is lived out in classrooms of higher education? What are the connections between Indigenous pedagogy and resilience? These are the questions that need answering as we move towards Indigenization. Drawing on themes emerging from my doctoral research as well as my own classroom pedagogy, I propose that resilience, from an Indigenous perspective, is process-oriented and based in sources of inspiration which strengthen our determination to endure and, eventually, succeed. In this session, I present how resilience from an Indigenous perspective emerges through interactional and reciprocal processes between student and instructor. Additionally, I consider the ways through which Indigenous pedagogy can intentionally foster student resilience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.059 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".