Knowledge Mobilization in Community-based Arctic Research
Bibliographic record
Abstract
Knowledge mobilization (KMb) is widely recognized as being essential to research, but there is limited academic guidance on how to do this well. This paper builds on the growing body of literature to develop a framework of key principles for KMb focused on Indigenous communities in the North American Arctic. We used a literature search and coding of identified good practice from both the grey and peer-reviewed literature (n = 80), alongside semi-structured interviews (n = 24) with key stakeholders to determine a framework of key principles and to contextualize and identify gaps or challenges. We found that effective KMb occurs throughout the research process and varies widely across regions and by researcher and community. Ultimately, there is no checklist of specific actions to ensure effective KMb, nor would such a list be desirable given the need to tailor KMb to specific contexts. However, we have identified three key principles of effective KMb: 1) respect, 2) mutual understanding, and 3) researcher responsibility. Underlying these principles is the consideration of trust and relationship building. Though these notions are based on subtle and nuanced context and vary from place to place, they all involve the consideration of formal and informal processes of KMb with Arctic research. By highlighting these key principles, we provide a framework to increase effectiveness of KMb across environmental change research within Arctic communities.
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
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 | 0.123 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.029 | 0.032 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".