The Use of Graphic Facilitation to Support Adherence to OCAP® Principles in Research With Indigenous Communities
Bibliographic record
Abstract
Graphic facilitation is a creative, robust visual communication process and tool that can be used by researchers for several benefits including improving data integrity; mitigating barriers between researchers and participants; promoting participants’ ownership of data, decision-making, and creativity; and, co-creating knowledge, which is of particular interest among certain cultures and in some contexts. For Indigenous Peoples who traditionally use visual, oral, and narrative modalities as primary forms of communication, graphic facilitation is a methodology that aligns well with these modes of communicating. In this article, we describe our use of graphic facilitation in a community-led project exploring Indigenous parents’ perceptions of community strengths, needs and priorities related to healthy early childhood development and optimal parenting. In collaboration with the Indigenous Friendship Centre in Hamilton, Canada, we held a Community Gathering that was facilitated by a graphic artist experienced in working with the Indigenous community; the findings resulting from the Gathering are presented. We discuss how researchers can use graphic facilitation as a tool to ensure adherence to the OCAP® principles of data ownership, control, access, and possession for the Indigenous community and describe the potential for mitigating power imbalances. Finally, considerations for researchers contemplating using graphic facilitation as a tool for research projects with Indigenous people and communities are presented.
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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 | 0.065 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".