Ronte’nikonhrarò:roks A Gathering of Minds: Developing an Indigenous-led Research Partnership to Promote Child and Family Health through Storytelling
Bibliographic record
Abstract
This study details the process involved in leading the development of a health research partnership that upholds the right to self-determination of Indigenous Nation-based research partners in collaboration with institutional-based research partners. The journey to formalize the partnership involved long-term relationship-building efforts with Indigenous organizations and Indigenous leaders. The focus of the study was to examine Indigenous child and family health experiences. The research process was guided by an Indigenous Research Paradigm (IRP) - informed by Indigenous traditional Knowledge and research practices. The research study was guided by Indigenous community members of the partnering First Nation. The IRP was tailored to represent traditional healing principles, and community-led research protocols. The study’s IRP engages the practice of Indigenous Storytelling methods (data collection) and the meaning-making process involved in Storytelling methods - the Storywork (analyses). Overall, the IRP creates space for Indigenous Peoples to share their Knowledges from their own Worldviews and upholds the Indigenous right to self-determination by promoting traditional healing methods of Storytelling and Storywork. The research activities were facilitated through collaboration between the CAC and I, by conducting community-based Storytelling circles with families in the First Nation. The study focused on the early years program for children and families. In person, sharing circles were hosted in partnership with the storytellers in a culturally significant space – the local Cultural Healing Centre at Wáhta’ Mohawk Territory. The Storytelling practice enabled the storytellers to share their own meanings of their lived experiences and depictions of unique historical contexts, that shape their contemporary health perspectives to inform the health and educational priorities in the First Nation. The stories were iteratively analysed through Storywork processes and informed collective recommendations and additional health and educational research priorities for the community. Overall, I describe my experience in navigating the development of an Indigenous Nation-based research study, within a Western-health science research degree program, and report the community recommendations mobilized from the Storywork.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".