Broken Circle: Exploring Indigenous Perspectives of Academic Integrity
Bibliographic record
Abstract
Across the land now known as Canada, a growing body of research confirms the importance of academic integrity in higher education. Indigenous voices, though, are largely subsumed within the morass of dominant student and faculty perspectives or segregated alongside international student perspectives. Using the imagery of the Medicine Wheel as a framework, this session explores the views of Indigenous faculty, staff, administrators, and graduates affiliated with a mid-sized post-secondary institution in British Columbia. Findings from a small-scale research study reveal a holistic vision of academic integrity that emphasizes relationships with people and knowledge. As Wilson (2008) explains, “relationships do not merely shape reality, they are reality” (p.7). In this relational paradigm, academic integrity is inseparably grounded in the broader principles of integrity, and relies on reciprocal truth-telling to maintain the wholeness of the circle (Lindstrom, 2022). In this session, participants will gain insights into the ways dominant approaches to academic integrity can break the circle of integrity. The session will review similarities and differences between the experiences of Indigenous and non-Indigenous learners, and will consider how Indigenous views of relationality may foster a culture where stewardship of knowledge strengthens the bonds of integrity for all.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.056 | 0.038 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".