Facilitating Co-Production of Transdisciplinary Knowledge for Sustainability: Working with Canadian Biosphere Reserve Practitioners
Bibliographic record
Abstract
Sustainability scientists argue that diverse knowledge holders should work together in social learning processes to co-produce knowledge in support of sustainability. Yet, how to co-produce such knowledge remains unexplored. A multi-year, national knowledge sharing partnership among Canadian biosphere reserve practitioners, academic researchers and policy advisors revealed that a skilled facilitator was necessary for successful knowledge co-production. We draw attention to the multiple barriers to learning and knowledge co-production, and to the skills required of the facilitator to address them. The facilitator helped draw together local and formalized western knowledge systems (weaving) and diffuse innovations across local sites (out-scaling) and between local sites and the broader program network (up-scaling). Our experience reveals that simply bringing parties together will not generate transformative change for sustainability. Rather, multi-lateral facilitators are needed to ensure deliberate and managed interventions and to institutionalize learning across a diverse collective.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".