Grounded governance: Cultivating social justice through community-engaged learning with PEPAKEN HÁUTW (Blossoming Place)
Bibliographic record
Abstract
By deepening student relationships to local governance systems, they become better equipped to envision and enact the worlds they seek to create. This paper discusses a community-engaged learning initiative facilitated by a University of Victoria instructor that centered on the theme of “Grounded Governance” to anchor student learning about governance and social justice theories, movements and practices. The overall approach draws inspiration from bell hooks who explains the value and significance of engaged learning. Specifically, this paper discusses the experience of teaching place-based learning through engagement with Indigenous-led ecological restoration efforts, including collaboration with PEPAKEṈ HÁUTW̱ (Blossoming Place). The author reflects on community site visits with a W̱SÁNEĆ nonprofit organization to learn about ecological restoration and decolonial governance. This grounded approach to teaching is inspired by Indigenous-led place-based learning. As a collaborative effort between the Studies in Social Justice program, the School of Public Administration and community partners, this pedagogy aims to integrate community-engaged learning more deeply into the study of public administration. Doing so in practice emphasizes relationship-building with local Indigenous-led community organizations while supporting student learning objectives by teaching about social justice through collaborative and meaningful public engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".