Partnering with Indigenous student co-researchers: improving research processes and outcomes
Bibliographic record
Abstract
OBJECTIVE: To examine the contribution of student co-researchers to a community-based participatory Photovoice investigation of Indigenous children's food-related lived experience. We examine co-researchers' contributions to the research process, their role in knowledge co-generation and dissemination, and factors that fostered research partnership with the teenage co-researchers. METHODS: High school students attending a First Nation community school in Canada were trained as research partners. They contributed to aspects of research design, conducted interviews with grades 3 and 4 Photovoice participants, and participated in data analysis and the development of a culturally relevant photobook. The study was initiated by the community's research committee. It is informed by critical consciousness theory and the positive youth development framework. RESULTS: Student co-researchers incorporated culturally appropriate strategies as they interviewed participants. Co-researchers adopted conversational approaches, built rapport by articulating personal and cultural connections, and engaged in mentoring and health promotion as they interviewed participants. They made critical contributions to dissemination by developing photobook content that promoted the importance of traditional foods and the vital role of family and community in healthy eating practices. Relationships and "dialogic" space were important to building partnership with and promoting capacity development among youth co-researchers. CONCLUSIONS: Partnership between university researchers and Indigenous student co-researchers holds great promise for health promotion in communities. Co-researchers developed research and leadership skills, gained understanding of health challenges facing their community, and initiated health and cultural promotion through the project's Photobook. This investigation supports the powerful potential of student co-researchers to meaningfully contribute to research processes and to build knowledge that is relevant and credible both within and outside of their communities. Findings have implications for youth, communities and researchers.
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 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 | 0.323 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".