How can research partnerships better support local development? Stakeholder perceptions on an approach to understanding research partnership outcomes in the Canadian Arctic
Bibliographic record
Abstract
ABSTRACT Understanding the benefits and outcomes of Canada's public investment in Arctic science and associated community–researcher partnerships represents a significant challenge for government. This paper presents a capital assets-based approach to conceptualising northern research partnership development processes and assessing the potential outcomes. By more explicitly considering the pre- and post-partnership asset levels (that is, social, human, physical, financial and natural assets) for different collaborators, the potential benefits and challenges associated with community–researcher partnerships can be collaboratively assessed. In order to help refine this approach, we conducted a survey of those involved in developing and maintaining community–researcher partnerships across Arctic Canada. Results indicate that the proposed approach could be useful for research funding agencies seeking to better understand partnership outcomes and promote more effective community–researcher interactions. Challenges include adequately capturing the qualitative nature of different capital assets, pointing to future research and policy needs. Better understanding the role of research in northern development has the potential to improve northern research, policy and practice.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".