Enhancing knowledge mobilisation and community engagement for research in Inuit Nunangat: A case study of participatory policymaking and collaborative research partnership in Nunatsiavut
Bibliographic record
Abstract
This thesis proposes a relational approach to enhance knowledge mobilisation and community engagement for research in Inuit Nunangat. The key principles of respect, mutual understanding, and researcher responsibility provide a foundation for effective knowledge mobilisation while acknowledging the need for context-specific adaptations. Using a case study, this work evaluates a collaborative research partnership with Nunatsiavut Government using participatory scenario planning for community-engaged policymaking. Participatory scenario planning is a robust tool for fostering hope, awareness, and collective responsibility. Furthermore, the evaluation underscores the importance of reconciliation, urging researchers to act in line with their values, be accountable to their relationships, and prioritise personal connections and trust-building. By adopting a relational approach and integrating these key principles, researchers can contribute to positive and ethically grounded knowledge outcomes while supporting Inuit self-determination in research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".