Towards new modes of knowledge exchange for sustainability transformations: an exploration of multi-sited dialogue as conferencing practice
Bibliographic record
Abstract
Actors working on global climate and sustainability challenges are faced with two competing imperatives: first, there is an ever-expanding body of knowledge, networks and initiatives generating new insights that should be shared. Second, we see a growing recognition that fly-in, fly-out conferencing practices are an insufficient and unsustainable model for learning, boundary crossing and collaboration towards sustainability transformations. Against this backdrop, we argue that knowledge exchange for societal transformations needs to consider three interrelated dimensions: (1) equity and inclusion – access to and representation in both process and content for all, (2) low carbon – limits the ecological burden produced by the exchange, and (3) impact – outcomes at individual and collective levels that enhance our ability to act. How we navigate the tensions that may emerge from these dimensions is a matter of pressing importance. This research article examines the potential of multi-sited dialogues as an approach to co-producing transdisciplinary solutions by using the three dimensions as the analytical framework. We report on a series of dialogue-focused conference sessions convened at three international conferences in 2023. Our findings describe the contributions that the multi-sited dialogue process brought to knowledge co-production across space and time, and the contribution of facilitation practices to the outcomes of these dialogues. We also introduce and discuss the set of principles for transforming sustainable conferencing practices that were co-produced over the three dialogues.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".