The Usage of Indigenous Languages as a Tool for Meaningful Engagement With Northern Indigenous Governments and Communities
Bibliographic record
Abstract
The Canadian Northern Corridor (CNC) program integrates formal academic research and a strategy of engagement with potentially impacted communities (Fellows et al. 2020). Finding common ground among Indigenous peoples, governments and industry on engagement and consultation practices is imperative to the future of resource development and the Canadian economy, and ultimately to the reconciliation of the relationships between Indigenous Peoples and Canada (Boyd and Lorefice 2018). In this paper, we focus on language, stressing that languages are more than just tools. Rather, all communicative systems also hold both individual and cultural identities, histories and memory, and encode knowledge in specific ways. This article investigates how Indigenous languages can contribute to meaningful engagement particularly within the context of the CNC concept; our recommendations also work toward strengthening existing Indigenous policy initiatives in Canada, uplifting Indigenous worldviews, and potentially supporting the reconciliation process. We draw upon primarily Indigenous scholars in explaining the reasons why using Indigenous languages matters for fostering meaningful engagement during research, consultation, and community engagement activities and address methods by which they can be implemented. After examining some past/ongoing attempts at this incorporation, we identify in our policy recommendations five different ways that the entire process of community engagement can align with the usage of Indigenous languages.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.044 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| 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".