P2-S3.12 Culture and research: how do you mix them?
Bibliographic record
Abstract
Background Community-based participatory research principles partner community and academics through all stages of the research process. Our purpose is to describe how we have been combining traditional Cree cultural practices (culture) and Western academic research processes (research) to facilitate a restorative research experience. Methods Culture and research were given equal emphasis in the structure and content of meetings, governance structure of the project, knowledge exchange activities, project development and ethics. To begin this sexual health project, the Health Director invited academic and government partners to the community. Statistics on rates of sexually transmitted infections were shared with community partners. Since then, emphasis has been placed on face-to-face meetings in the community. Elders and community leadership have attended meetings and provided guidance on research activities. The governance structure was organised so primary decision makers are from the community and are guided by a community working group, cultural advisor and scientific advisor. Knowledge exchange was achieved through mutual participation in traditional Cree cultural activities and research emersion at an academic institution. Key community informants, Elders and research assistants led project development. The local First Nations College provided primary ethical review. Results Face-to-face meetings helped build strong, sustainable relationships between community and outside researchers. Knowledge exchange activities, like cultural teachings and research trainings, contributed to mutual respect, understanding and trust between community and outside team members. Participation in community events and traditional ceremonies combined with formal and informal discussions with the community working group, Elders, and key community informants led to the identification of a priority research area of importance to the community. Following community led ways of knowing and doing has led to the development of innovative research methods for data collection. Ethics review held in ceremony provided a supportive, holistic environment from which to proceed with research activities. Conclusions Building trust and nurturing the relationship between community and outside research partners has been integral for restoring confidence in the potential benefits of research for this First Nations community.
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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 | 0.060 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.047 | 0.019 |
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".