Fuzzy SETS: acknowledging multiple membership of elements within social-ecological-technological systems (SETS) theory
Bibliographic record
Abstract
Convergent research to tackle complex, wicked problems requires synthesis across multiple sectors and disciplines, but epistemological, ontological, and linguistical disagreements between disciplinarily diverse research teams can hinder the progress of transdisciplinary team efforts. For example, in social-ecological-technological systems (SETS), elements within the system may require distinction between component (S-E-T) parts to be conceptualized and modeled. Current SETS literature has focused predominantly on the deep interconnections across these social, ecological, and technological elements, but has not addressed how to explicitly acknowledge potentially messy, multi-membership classifications of elements within these categories. We introduce the conceptual framework of Fuzzy SETS, drawing on mathematical fuzzy set theory and SETS literature. By treating these categories as “fuzzy,” or being capable of multiple memberships, we investigate how the conceptual framework of fuzzy SETS can facilitate convergent, collaborative research across multiple disciplines and epistemologies by explicitly acknowledging and visualizing differences and similarities in perception of a given SETS. We apply this framework to our own work of creating a system dynamics model of the Santa Fe Watershed, New Mexico. Within our network of researchers, diverse perspectives exist when categorizing elements within the Santa Fe Watershed into social, ecological, and technological categories. Our findings support the hypothesis that the fuzzy SETS conceptual framework is a way to honor a diversity of epistemological perspectives within transdisciplinary teams by explicitly accepting that different views can coexist and can actually enrich our understanding of systems by creating a basis for asking deeper questions regarding their elements and dynamics.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".