Four analysis moments for fuzzy cognitive mapping in participatory research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fuzzy cognitive mapping (FCM) is a practical tool in participatory research. Its main use is clarifying causal understandings from several knowledge sources. It provides a shared substrate or language for sharing views of causality. This makes it easier for different interest groups to agree what to do next. Each map is a collection of causal relationships with three elements: factors (cause and outcome), arrows linking factors, and weights indicating the perceived influence of each cause on its outcome. Stakeholder maps are soft models of how they see causes of an outcome, such as access to services or systemic racism. Based on a standardized FCM protocol, we present four moments in FCM analysis. (1) Agree shared meaning across maps. (2) Calculate the maximum influence of perceived causes. (3) Simplify the maps for communication. (4) Identify priorities for action. We provide explanations of the four moments in FCM analysis, with examples from five countries. FCM offers a practical means to guide health action. It incorporates local perspectives with transparent and traceable procedures.
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.
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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it