Examining research policy and practice in Canada’s North to support evidence-based decision-making
Bibliographic record
Abstract
Research has a long and contentious history in the North, particularly with Indigenous northerners who have felt marginalized and excluded. Currently, there is movement towards a research agenda guided by northern-identified priorities and inclusive of northerners leading research. While recent studies demonstrate poor alignment of northern research with recognized societal needs, there has never been a broad systematic investigation of northern research policy or practice. In light of these gaps in understanding, this study focused on exploring the research-policy interface in the Canadian North, including Nunatsiavut, Nunavik, Nunavut, the Northwest Territories, and the Yukon. This thesis focused on the overarching research question: How do the policies that guide research in Canada’s North support or limit research practices that align with northern priorities and knowledge needs? A document analysis of northern policies established foundational knowledge of research expectations and priorities. A critical analysis of pan-territorial research licensing data developed a baseline understanding of the current research context. Employing semi-structured interviews, the experiences and perspectives of northern and southern-based research practitioners were analyzed within the northern research policy context. This study exposed a lack of transparency in the extent to which Indigenous and northern voices inform national and territorial research policies, identifying the need for increased northern input in developing research programs. There are significant gaps in research about northern Canada including uneven geographic distribution of research, underrepresentation of health research, and misalignment with northern priorities. Although there have been investments in research across the three territories, northern leadership is still underrepresented. This study identified a number of governance, policy, institutional, capacity, funding, and regulatory factors that limit research practice and ultimately the integration of research in policy. Funding continues to be the main limiting factor to the engagement of northerners in research, along with continuity in funding being an ongoing challenge for northern organizations. Multi-scalar, inclusive partnerships have the potential to broaden the impacts and benefits of northern research, despite the complexities of conducting partnered research. Partnership approaches that align research design with partner needs are best positioned to support the integration of research outcomes in policy development.
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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 | 0.068 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| 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".