Co-Production and Collaboration: Examining the Application of Co-Production of Knowledge Principles and Practices at the Arctic Rivers Summit in Anchorage, Alaska
Bibliographic record
Abstract
<p dir="ltr" style="line-height: 1.38; text-indent: 36pt; margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 12pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">In the face of climate change impacts in the Arctic and sub-Arctic regions of Alaska and northwest Canada, there is a growing need to improve collaboration between Indigenous communities and western scientists to address multifaceted socio-environmental problems. These problems include shifts in the freezing of river-ice transportation corridors, permafrost thaw, and decreased salmon abundance in the Yukon River. These environmental problems pose drastic impacts for Indigenous communities in the region who depend on rivers for transportation, subsistence fishing, and cultural heritage. Addressing these challenges holistically and equitably requires employing a co-production of knowledge framework for producing useful and usable knowledge. This process requires high capacity for and commitment to building relationships between Indigenous Knowledge holders and western scientists. The Arctic Rivers Project is a multidisciplinary project led by Yukon River communities and organizations, the University of Colorado, Boulder, and the United States Geological Survey. The project attempts to employ a co-production of knowledge framework to understand climate change impacts in Alaska’s Yukon River basin with the goal of producing useful and usable knowledge.</span><strong id="docs-internal-guid-5b3086ff-7fff-17a6-0d73-720ca080a11e" style="font-weight: normal;"></strong> <p dir="ltr" style="line-height: 1.38; text-indent: 36pt; margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 12pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">In this work, I explore the application of co-production of knowledge principles and practices in the planning, preparation, and execution of the Arctic Rivers Summit, held by the Arctic Rivers Project in Anchorage, Alaska, December 6-8, 2022 to bring together Yukon River stakeholders and community members. I write a narrative of the preparation and sessions held at the Arctic Rivers Summit, analyzing them for their applications of co-production of knowledge principles and practices. I then explore the extent to which co-production of knowledge principles were employed, the scalability of co-production efforts, and challenges in co-production of knowledge between western scientists and Indigenous communities. In my analysis, I found that co-production practices were utilized throughout the process, beginning with the event’s co-production by the Arctic Rivers Project research team, Indigenous Advisory Council, and the Institute for Tribal Environmental Professionals. The processes that were used in planning, preparing for, and executing the Arctic Rivers Summit serve as an example for how academic institutions can work to integrate co-production of knowledge processes and practices into events and research projects with Indigenous communities.</span>
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".