Conceptualizing the Role of a Strategist for Outreach and Indigenous Engagement to Lead Recruitment and Retention of Indigenous Students
Bibliographic record
Abstract
A number of universities have introduced Indigenous student-specific programming to improve recruitment. These programs target the needs of Indigenous students and often impart a sense of comfort or belonging that may be more difficult to obtain in a mainstream program. The University of Saskatchewan, College of Nursing, implemented a Learn Where You Live delivery model that challenged the university community to think differently about outreach and engagement. This is best described by redefining distance such that student services and supports would no longer be localized to a main campus but redesigned for distribution across the province. Sustaining this model meant the College leadership had to find new ways to support faculty to engage in teaching and learning opportunities that would be context relevant and aid student recruitment and retention. The new position of Strategist for Outreach and Indigenous Engagement was created to lead opportunities for faculty and staff to gain knowledge and expertise in policy development, negotiation and implementation for success in the distributed delivery model. The framework of Two-Eyed Seeing was adapted to guide the introduction and ongoing implementation (Bartlett et al. 2012).
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".