Unsettling the Familiar: Experiential Human Rights Learning through Civic Monuments at the University of Winnipeg
Bibliographic record
Abstract
Abstract Research shows that experiential learning improves student learning, engagement and retention and yet, despite growth in university undergraduate human rights programmes, relatively little is known about experiential learning in the human rights classroom. This article examines a low-cost and online-adaptable experiential learning assignment focused on civic statues and monuments in Winnipeg, Canada. The assignment and supporting activities, speakers and resources were implemented in thirteen sections (online and in-person) of a prerequisite-free undergraduate human rights course over two academic years. Surveys were circulated to students before and after the assignment examining their perspective on key concepts and their own skill and capacity. As a result of the activity students indicated a heightened understanding of the role of monuments and representation as human rights issues, a broadened interpretation of expertise in relation to monuments, and an increased self-perception of skills and capacity related to monument analysis. This article contributes to the emerging literature on human rights education in higher education and to the need for accessible, scalable, and online-adaptable forms of experiential learning. Human Rights is a growing field at the undergraduate level, and our project can usefully contribute to the literature and practice of human rights pedagogy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".