Strengthening Administrative Structures to Enhance Community-Partnered Research
Bibliographic record
Abstract
Post-secondary institutions across Canada are grappling with how to advance reconciliation and equity in education and research. This challenge is amplified in northern Canada, where northern residents have long sought equity in the Arctic and northern research ecosystems, yet the population has historically been excluded from research programs and research leadership. This paper is focused on exploring how a community college in northern Canada should invest in developing institutional support for research and expanded research capacity as it transforms into a polytechnic university. The community college serves a remote population where the majority of residents are Indigenous, and the institution needs to develop capacity in a way that supports both Indigenous self-determination in research and the regional aspirations of expanded research leadership. A solution is proposed that uses transformative and inclusive leadership approaches to guide the development of research services and capacity at the college in partnership with Indigenous and community organizations through the co-development of a partnership framework. The Dissertation-in-Practice (DiP) presents a change implementation plan to develop the proposed solution in collaboration with Indigenous partner organizations. This plan describes how the college can establish institutional support for Indigenous self-determination in research and build research capacity grounded in equity and partnership. Ultimately, this DiP suggests developing a partnership framework to guide the co-development of research capacity in collaboration with Indigenous community partner organizations to empower, strengthen, and uphold a network of community-led research partnerships.
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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 | 0.122 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".