Northern Research Policy Contributions to Canadian Arctic Sustainability
Bibliographic record
Abstract
Academic research plays a key role in developing understanding of sustainability issues in the Canadian Arctic, yet northern organizations and governments struggle to find research that is relevant, respectful of local interests, and that builds local capacity. Northern science and research policies communicate expectations for how research should be prioritized, planned, conducted, and disseminated. They discuss northern leadership of research and outline the diverse roles that northerners and northern organizations could fill in research programs and projects. Many of these documents are founded on the need for research to improve environmental, economic, and social sustainability in the Canadian North and provide insight into how academia can support a northern-led Arctic sustainability research agenda. The goal of this study is to examine northern research-policy documents to identify commonalities amongst the goals and priorities of northern organizations and their shared expectations for research in northern Canada. The objectives are to understand how organizations expect researchers to engage in and conduct research, how research programs can align with northern science policy objectives, and how academic research can support policy and decision-making related to sustainability. Through a quantitative content analysis combined with a qualitative thematic analysis, this comprehensive review examines research policy, strategy, guidance, and program documents produced by northern and northern-focused governments and Indigenous organizations. Relationships, partnership, and communication are the foundations of relevant and applicable research, requiring both resources and time for local and partner participation. Our analysis shows that researchers should consider potential policy applications for sustainability research early on in the development of research projects, ensuring that relevant local and policy partners are involved in designing the project and communicating results.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".