Convergence, transdisciplinarity, and team science: an interepistemic approach
Bibliographic record
Abstract
The challenges facing the Intermountain West are characterized by extreme complexity and enormous consequences. They include climate change and associated ecological effects, such as catastrophic wildfire and drought. They are also inextricably linked to social inequities, including freshwater availability, land conversion, and access to basic human needs such as quality food, affordable energy, and access to healthcare. A meaningful response to these challenges requires new thinking. Convergent research is designed to foster new thinking by creating novel frameworks and conceptual models that drive innovation. Here, we share our approach to convergent research in the Transformation Network (TN), a National Science Foundation supported Sustainable Regional Systems Network. A key element of the TN’s design is an interepistemic and even interontological approach that builds across different knowledge systems throughout academia and among Native American and community partners. After first providing an overview of the development of the field of convergence research and its relationship to transdisciplinary research, we provide an outline of the TN’s approach, which draws from two schools of transdisciplinarity thought—the metaphysical approach of the Nicolescuian School and the more solution-focused Zürich School. We then explain how we operationalize our approach with systems thinking and systems dynamics modeling, as well as community engagement, diversity, equity, inclusion, and justice efforts, and continual learning with reflexive assessment and training practices. This includes an example where TN faculty and students partner with members of the Navajo Nation to support the independence of Native American communities in the San Juan River Watershed through the implementation of small-scale sustainable off-grid food-energy-water systems.
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.
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 | 0.027 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".