Contexts, Conditions and Methods Conducive to Knowledge Co-Production:\tThree Case Studies Involving Scientific and Community Perspectives in Arctic Wildlife Research
Bibliographic record
Abstract
Decision-makers require current and robust information to address the effects of social-ecological changes facing ecosystems, wildlife, and humans; however, research defined by single disciplines and knowledge systems is often challenged in fully representing the complexity of such problems. There is a recognized need to include the perspectives of academic and local knowledge holders in research as evidence argues this can produce more robust knowledge and lead to greater public acceptance of policy. Knowledge co-production has been proposed as a research approach that can include academic and non-academic actors in addressing complex problems that transcend disciplinary and epistemological boundaries and have societal and scientific significance. While knowledge co-production has gained attention in environmental research in many regions, its application has not been extensively explored in the Arctic. \n \nThis research used a case study approach to examine the contexts, conditions, and methods that support knowledge co-production on wildlife issues with Canadian Arctic communities. Three cases were selected to examine knowledge co-production in the context of a past research study, an ongoing study, and to consider the pre-conditions necessary for knowledge co-production to benefit future research. Data collection included semi-structured interviews, workshops, and participant observation with scientists and Inuit community members involved in ringed seal research in Kugaaruk and Iqaluit and fisheries research in Pangnirtung, Nunavut. \n \nResults indicate that Arctic wildlife research can benefit from knowledge co-production. There are particular structural and process conditions that help facilitate successful knowledge co-production and establishing these conditions requires deliberate work on the part of researchers and community members involved. Establishing shared goals and problem definitions, creating the space to identify and share positionalities and perspectives on issues, and clarifying roles of academic and community actors all emerged as important conditions in the cases. Further, results suggest that semi-structured interviews and purposefully designed and facilitated thematic workshops provide the flexibility to create the time and space needed for participants to learn about and engage with one anothers values, perspectives, and priorities. This research shows that when effort is made to establish the necessary conditions for knowledge co-production early on in the research process, projects can produce knowledge that is perceived as more credible, salient, and legitimate by all involved.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.037 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".