Navigating the Shifting Landscape of Engagement in Northern Research: Perspectives from Early Career Researchers
Bibliographic record
Abstract
Advance Online Article published December 16, 2022An examination of research in northern Canada and its ties to extractive, colonial practices has been highlighted in recent years, alongside heightened expectations for community- and Nation-engaged practises. Here, we explore the diverse ways that northern-focused early career researchers (ECRs), from a range of faculties, life experiences, and disciplines, engage with the communities and Indigenous Nations they work in and, more broadly, the knowledge they have gained from conducting research in the North. Scholars in the fields of education, anthropology, and renewable resources from the University of Alberta share their experiences to discuss 1) approaches to meaningfully and respectfully engaging with communities and Nations in the North; 2) knowledge translation and mutual capacity building; and 3) responsibilities and accountabilities for engaging with communities and Nations. We find resonance with the Five R’s of research—relevance, reciprocity, respect, responsibility, and relationship—that help ensure Western-derived knowledge benefits the communities and Nations that ECRs work alongside.
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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 | 0.075 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.037 | 0.049 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".