Critical pedagogical designs for SETS knowledge co-production: online peer- and problem-based learning by and for early career green infrastructure experts
Bibliographic record
Abstract
Despite a growing understanding of the importance of knowledge co-production for just and sustainable urban transformations, early career green infrastructure experts typically lack opportunities to practice transdisciplinary knowledge co-production approaches within their normal training and professional development. However, using online collaboration technologies combined with peer- and problem-based learning can help address this gap by putting early career green infrastructure experts in charge of organizing their own knowledge co-production activities. Using the case study of an online symposia series focused on social-ecological-technological systems approaches to holistic green infrastructure implementation, we discuss how critical pedagogical designs help create favorable conditions for transdisciplinary knowledge co-production. Our work suggests that the early career position offers a unique standpoint from which to better understand the limitations of current institutional structures of expertise, with a view towards their transformation through collective action. Supplementary Information: The online version contains supplementary material available at 10.1186/s42854-023-00051-1.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".