Critical pedagogical designs for SETS knowledge co-production: online peer- and problem-based learning by and for early career green infrastructure experts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it