Nurturing communities of practice for transdisciplinary research
Bibliographic record
Abstract
Transdisciplinary research practice has become a core element of global sustainability science. Transdisciplinary research brings with it an expectation that people with different backgrounds and interests will learn together through collective problem solving and innovation. Here we introduce the concept of "transdisciplinary communities of practice, " and draw on both situated learning theory and transdisciplinary practice to identify three key lessons for people working in, managing, or funding such groups. (1) Opportunities need to be purposefully created for outsiders to observe activities in the core group. (2) Communities of practice cannot be artificially created, but they can be nurtured. (3) Power matters in transdisciplinary communities of practice. These insights challenge thinking about how groups of people come together in pursuit of transdisciplinary outcomes, and call for greater attention to be paid to the social processes of learning that are at the heart of our aspirations for global sustainability science.
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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 | 0.065 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".