Integrating knowledge systems for holistic approaches to addressing knowledge and health inequities: combining fuzzy cognitive maps
Bibliographic record
Abstract
This paper describes fuzzy cognitive mapping as an accessible and robust tool to strengthen community engagement in health promotion research. We outline how fuzzy cognitive mapping can combine, compare, and contextualize knowledge and priorities from diverse population groups as well as from evidence syntheses. We present procedures to represent a shared perspective across populations or population groups through reconciling maps by simple or weighted averaging. We present a novel second approach to reconciling derived from discourse analysis. We then present two procedures to contextualize one knowledge in another knowledge. The first procedure draws on Bayesian updating, providing a formal way to account for stakeholder knowledge in contextualizing other knowledge sources, including evidence syntheses. A second approach compares discourse patterns across maps derived from different sources. We provide examples of each procedure, describe how each may contribute to greater incorporation of patient- and community-level input in decision-making, and share tools for researchers interested in applications of fuzzy cognitive mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".