“Reconciliation” in undergraduate education in Canada: the application of Indigenous knowledge in conservation
Bibliographic record
Abstract
Both the Truth and Reconciliation Commission (TRC) and the National Inquiry into Missing and Murdered Indigenous Women and Girls (MMIWG) explicitly emphasized the role of educators in “reconciliation.” Alongside this, conservation practitioners are increasingly interacting with Indigenous Peoples in various ways, such as in the creation and support of Indigenous protected areas and (or) guardian programs. This paper considers how faculty teaching aspiring conservation practitioners can respond appropriately to the TRC and MMIWG Inquiry while preparing students to engage with Indigenous Peoples in a way that affirms, rather than questions Indigenous knowledge and aspirations. Our argument is threefold: first, teaching Indigenous content requires an approach grounded in transformational change, not one focused on an “add Indigenous and stir” pedagogy. Second, we assert that students need to know how to ethically engage with Indigenous Peoples more than they need knowledge of discreet facts. Finally, efforts to “Indigenize” the academy requires an emphasis on anti-racism, humility, reciprocity, and a willingness to confront ongoing colonialism and white supremacy. This paper thus focuses on the broad change that must occur within universities to adequately prepare students to build and maintain reconciliatory relationships with Indigenous Peoples.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".