The challenge of meaningful knowledge mobilization of climate change research in the Canadian Arctic for early-career researchers
Bibliographic record
Abstract
Communication of research related to climate change in a way that is meaningful and respectful to Indigenous Peoples is challenging. While engagement with Indigenous communities is now increasingly incorporated into the expected standard of research processes in academia, early career researchers face challenges such as funding limitations, extensive regulatory processes, and timeframes that exceed the duration of a normal graduate-level degree. To better understand the obstacles that early career researchers are faced with, and subsequently provide some guidance on how these barriers can be mitigated, six interviews with practitioners of knowledge mobilization in the Canadian Arctic were conducted. Participants suggested that, while communicating knowledge purposefully depends largely on the research context and communities involved, researchers are encouraged to be well-informed, resourceful, and flexible in their research approaches. By applying these recommendations outlined by experienced practitioners, and reviewing academic literature, early career researchers can mitigate logistical and cultural barriers and communicate knowledge in a more culturally sensitive manner. More community-based research is needed to continue to enhance the understanding of how to mobilize knowledge on climate change in a meaningful way, to create more informed guidelines and support systems, and to make them widely accessible to researchers at all stages of their careers.
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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 | 0.090 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.085 | 0.036 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".