Addressing communication challenges in transdisciplinary sustainability science: insights from a case study
Bibliographic record
Abstract
To address sustainable development challenges, transdisciplinary sustainability science (TDSS) requires understanding and managing ecological processes that transcend scientific, geopolitical, and cultural divides. Communication that can bridge these divides is critical for the success of TDSS projects. We describe a communication strategy developed as part of a large, transdisciplinary, multiyear project that aimed to understand the impact of invasive trees (specifically, <em>Prosopis juliflora</em>) on human societies and ecosystems in eastern Africa and to develop and implement sustainable management solutions to mitigate those impacts. The strategy included 17 activities designed to support communication among scientists, students, and stakeholders from the project’s inception to its conclusion. Both the informational and relational dimensions of communication were considered in the design and implementation of these activities. We discuss the effectiveness of this communication strategy, offering it as a guide to enhancing communication and the success of large TDSS projects.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".