Development of a collaborative research framework: the example of a study conducted by and with a First Nations, Inuit and Métis womens's community and their research partners
Bibliographic record
Abstract
The lack of research that effectively addresses inequity within Canadian society is an indicator of the failure of mainstream research approaches and practices to engage with all populations. This paper describes the development of a collaborative framework defined by a First Nations, Inuit and Métis women’s community members and its research partners as ethical, useful and relevant. There were two essential phases in negotiating a collaborative framework for a community- research partnership and the steps in a community-based participatory approach described: 1) establish guiding features of a collaborative framework by forming an advisory group, developing ethical guidance, agreeing upon underlying theoretical concepts for the research study; and 2) engage in research actions that support co-creation of knowledge throughout study processes. The case study example used to illustrate the collaborative framework was conducted by and with a First Nations, Inuit and Métis women’s community and research partners to culturally adapt a health decision-making strategy. A community-based participatory research approach fostered engagement among community and research participants and directed community-research collaboration. The collaborative framework structured ongoing negotiations within the community-research partnership to ensure that ethical obligations to research participants and the broader community were met and goals of the study achieved.
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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 | 0.061 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".