Community-engaged Learning (CEL): Integrating Anthropological Discourse with Indigenous Knowledge
Bibliographic record
Abstract
The Indigenous Action Group (IAG) is an alliance of solidarity between Indigenous and settler faculty at the University of Toronto Mississauga with the Mississaugas of the Credit First Nation (MCFN), whose Treaty lands the campus is located on. This partnership of responsibility supports the MCFN goals of truth (through public knowledge and recognition of their history), and reconciliation (through the support and equitable sustenance of Indigenous pedagogy, knowledge systems, and research methodologies in educational institutions). The IAG has developed a Community-Engaged Learning (CEL) course to bring ontological pluralism to the Academy to legitimize Indigenous knowledges, epistemologies, and involve the placemaking of local Indigenous communities (Tuhiwah Smith, 2012). This second year undergraduate course entitled “Anthropology and Indigenous Peoples of Turtle Island (in Canada)” was developed and implemented by the Indigenous Action Group to prioritize first person voices from the local Indigenous community. We are hoping this diverse educational model will change the discourse in anthropology courses to begin a collective understanding of ongoing power imbalances and oppression in education from colonial mechanisms.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 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".