Bringing indigenous educational leadership perspectives into social work leadership education
Bibliographic record
Abstract
Abstract This article explores how educators benefit from partnerships with Indigenous peoples and collaborations across disciplines and positionalities in responding to policy calls to transform social work teaching environments to better reflect Indigenous voices. Drawing on an “Indigenous ethical space of engagement” (Ermine 2007: 194), the authors share their experiences collaborating on an Indigenous teaching project entitled “Matoogiying Gaminigowiziying (Sharing our Gifts): Indigenous Learning Bundles” at Western University. They worked together to introduce Indigenous educational leadership content into a graduate-level Administration and Supervision social work leadership course at King’s University College during the 2023 and 2024 academic years. Drawing on a conversational approach, the authors will begin by locating themselves, then reflect on social work leadership education, the gaps in Indigenous social work leadership research, and outline the value in drawing on Indigenous educational leadership research to fill gaps in teaching social work leadership. They will describe the creation and content of the Indigenous Learning Bundles, the social work education policy context, and how the leadership bundle was integrated into the graduate social work leadership course. To conclude, they will share personal insights they have garnered by reflecting together through a conversational approach centered on their collaborative teaching experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".