The Role of Relationships in Indigenous Community-engaged Learning
Bibliographic record
Abstract
Service Learning is the pursuit of education while serving the interests of the community, and undertaking a direct role in social change (Phillips, 2013). It allows students a chance to develop research skills while simultaneously building bonds with colleagues and creating relationships within communities. We are two undergraduate students, from a collaborative group of five, who undertook a month-long summer Scholars in Residence (SiR) program at the University of Toronto Mississauga. Our involvement with the SiR project, “Community-engaged Learning with the Indigenous Action Group (IAG)” immersed us in a grassroots effort to research the impacts of a community-engaged learning course at the student level. The goal of the course, “Anthropology and Indigenous Peoples of Turtle Island” (ANT241H) is to initiate and maintain an Indigenous curriculum in a university setting. Self-reflection is pivotal in our research skill development, and our understanding of the impact of the project in its initial stages. Qualitative analysis of student assignments demonstrated the importance of creating relationships with Indigenous Scholars to advance Indigenous Pedagogy and provide students with the tools needed to build relationships with the local Indigenous community. Our own relationships with the Mississaugas of the Credit First Nation (MCFN) was limited to remote learning by COVID-19 pandemic restrictions. Despite the hindrances to our relationship building, the SiR experience transcended our interpersonal needs as allies, and laid the foundation for a stronger bond as we progress in the longitudinal study.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".