Reflecting on the use of Concept Mapping as a Method for Community-Led Analysis of Talking Circles
Bibliographic record
Abstract
Indigenous peoples' active involvement in and ownership of research involving their communities are imperative to ensure that community values are prioritized and that research fosters self-determination of Indigenous health. To share our reflections about how concept mapping can be useful within the context of research with First Nations communities. Three First Nations communities participating in the Kahnawà:ke Schools Diabetes Prevention Project Community Mobilization Training (CMT) engaged in Concept Mapping (Kane & Trochim, 2007). Community Research Assistants provided testimonials about the process. The strengths of using concept mapping within this project align with the current literature that highlights it is very adaptable to Indigenous contexts, allows for high levels of engagement throughout the entire research process from design to dissemination, and thus strengthens ownership of the research project among community members. Concept mapping is relevant and useful for research with First Nations communities.
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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 | 0.091 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".