Local participation and partnership development in Canada's Arctic research: challenges and opportunities in an age of empowerment and self-determination
Bibliographic record
Abstract
ABSTRACT An important component of northern research in Canada has been a strong emphasis on local participation. However, the policy and permit landscape for community participation therein is heterogeneous and presents specific challenges in promoting effective partnerships between researchers and local participants. We conducted a survey of northern research stakeholders across Canada in order better to understand the benefits and challenges associated with research partnerships with a view to informing northern research policy and practice. We found that local engagement at the proposal and research design phases, the hiring of community researchers and engagement of local persons at the results dissemination phase were important factors affecting success. Respondents also indicated a lack of social capital (trust and reciprocity) between researchers and communities as placing a negative impact on science partnerships. Overall, researchers were perceived to benefit more from research partnerships than their community counterparts. Partnerships in northern research will possibly require further decentralisation of power to achieve the policy objectives of local community participation. This could be achieved, in part, by allowing non-academic principal investigators to receive funding, or by involving communities in research priority-setting, proposal review and funding allocation processes.
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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 | 0.056 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.054 | 0.023 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".